medical research report example

Clinical Researcher

Clinical Study Reports 101: Tips and Tricks for the Novice

Clinical Researcher September 15, 2020

Clinical Researcher—September 2020 (Volume 34, Issue 8)

PEER REVIEWED

Sheryl Stewart, MCR, CCRP

The tenets of Good Clinical Practice (GCP), promulgated by the International Council for Harmonization (ICH), require that investigator-initiated trials (IITs), especially those involving an Investigational New Drug application to the U.S. Food and Drug Administration (FDA), have the principal investigator (PI), the institution, and the study team assume roles of both the sponsor (ICH GCP E6(R2), Section 5) and of the PI (ICH GCP E6(R2), Section 4).{1} If you are part of an IIT team, whether you are the investigator, a clinical research coordinator, or someone working in any of the many other important roles within the team, you may be tasked with authoring a clinical study report (CSR) at one time or another within the course of the study. At the very least, you may be asked to contribute to, or provide peer review of the document before it is submitted for its intended purpose.

The purpose of this review is to provide a framework for study team members, whether it’s for a large team that includes regulatory and administrative support or for smaller teams with only one or two members, for writing and organizing the CSR.

First, is important to understand the definition, requirements, and potential uses of a CSR. The report is a comprehensive look at all the data produced in a clinical study, presented in text, tables, and figure formats. It will often include discussions and conclusions that provide context to the findings regarding the drug, device, biological product, surgical method, counseling practice, or any other type of therapeutic product or practice under study and where it may contribute to an improvement on the state of the art for treating or preventing a particular health condition.

If a study has prespecified endpoints or parameters, the CSR will report the current outcomes and statistical parameters for these endpoints. Key messages will be referred to and highlighted throughout. Key messages are important study findings that support the prespecified endpoints, supply proof of the justification of clinical benefit, or differentiate the study product from others in the therapeutic space.

Most likely you already appreciate the ethical responsibility a clinical study team has to clinical study data transparency, which for that reason alone would make the production of some sort of CSR necessary. Indeed, the preparation and representation of study progress is prescribed in the aforementioned ICH GCP E6(R2) guideline,{1} which states that study sponsors should ensure that clinical trial reports are prepared and provided to regulatory agencies as they are required.

Further, the guideline recommends study sponsors to rely on a subsequent guideline on Structure and Content of Clinical Study Reports (ICH E3).{2} Lastly, adhering to this ethical responsibility and following GCP have become mandated both in the U.S. and in Europe, where study data are expected to be recorded on ClinicalTrials.gov and the EudraCT database, respectively, for the sake of transparency and in support of further scientific inquiry, thus making the organization and preparation of study data in a prespecified format necessary.{3,4}

There are a few different uses for a CSR, though primarily it is utilized either to summarize the data and outcomes at the end of the study, or for marketing authorization. Those two purposes are specifically outlined in ICH E3 and ICH E6.{1,2} However, a CSR may also be written for third-party payer reimbursement purposes, providing details in support of clinical benefit. Because in most cases CSRs will ultimately have a regulatory reviewer, authoring a report that is consistent in formatting and content with what is expected will hopefully not only enable a smooth review, but also will facilitate proper data cleaning, presentation, and timeliness that make the document fit for purpose.

ICH E3 offers a CSR template to guide you in terms of providing the proper data and content in a specified order and format. This guideline can be found either on the ICH website or the FDA website.{2,5}

It is important to note that there are no requirements to follow the template precisely. Not every section is appropriate for every study, and because the overarching purpose of a CSR is to provide proper representation of the study data and any key messages you want to report, flexibility is allowed and encouraged in order to meet those important goals. However, for anyone new to the process of crafting a CSR, this template is a helpful starting point.

Transcelerate Biopharma, a nonprofit organization involved in researching means to increase efficiency and innovation in the pharmaceutical research sciences, also has interpreted the ICH template and has produced a useful tool to improve this reporting.{6} If the instruction and guidance in the ICH or Transcelerate templates do not meet your needs, or you have further questions as to how to properly represent the study data, the CORE reference manual (Clarity and Openness in Reporting E3-based) is another resource. It was produced in 2016 in response to regulatory changes for public disclosure of clinical study data, and can provide direction and interpretation of the ICH E3 template.{7}

For the novice author of a CSR, however, the ICH E3 template, coupled with the Transcelerate template, should provide a strong starting point for the project planning of the report, as well as the document formatting.

Sidebar: Tips and Tricks for Getting Started

Determining Stakeholders

Once you’ve reviewed the template and created a draft outline of the project, determine the key stakeholders with whom you’ll need to partner to complete this project. Likely you will need input from your clinical study management team, teammates responsible for data entering and cleaning, a biostatistician, any teammate or organization member able to perform literature reviews, those staff qualified to compose patient or adverse event narratives, and those team members who can help determine key messaging in this report. Lastly you will want to determine the group of key stakeholders who will be your final review team for the document—those who will help you finalize the document prior to submission.

Sidebar: Tips and Tricks for Stakeholder and Project Management

Determining Timelines

Once you have determined your key stakeholders, you will want to determine timelines to ensure steady progress continues to be made on the document. If you’ve chosen to utilize a scope document, you’ll want to include these timelines in it, so the entire team is aware of the project process, the timing requirements, and each gating item (key gating items are summarized in Figure 1).

Figure 1: Preparing, Writing, and Review of the Clinical Study Report—Key Gating Items

Time management is paramount for clinical trial submissions to regulatory authorities. Attendees at medical writing conferences over the course of a five-year period (2008 to 2013, n=78) were surveyed to determine to how long each step of the CSR process can typically require.{8}

To complete a “moderately complex” CSR for a Phase III study with 200 to 400 participants, the surveyed medical writers responded with a mean answer of 16.9 days from the receipt of the final tables, listings, and figures (TLFs) to delivery of the first draft of the CSR. They estimated a mean of 25.7 days from the first draft to the final draft routed for review. The time from database lock to completion was reported to be on average 83 days.

While there was a wide range for the timelines reported, these data provide the novice CSR author a basic reference point for how long the individual processes can expect to take with experienced medical writers. Fortunately, while TLFs are being crafted, multiple other “Writing and Document Review” tasks from Table 1 can be performed simultaneously.

At Last…the Writing!

Typically, the flow of your CSR will progress under six primary headings or sections, not unlike those used in a research manuscript. On the front end, even before the background and introduction, the document will include a title page, synopsis, table of contents, list of abbreviations, ethics statements, and details on the study’s administrative structure. The primary sections to come after that are highlighted in Figure 2 and summarized in turn below.

Figure 2: Primary Sections

Background, Intro. > Non-Results > Results > Discussion > Conclusion > Exec. Summary

Background and Introduction

When available, utilize any state-of-the-art analysis of the product/therapy from the protocol for your CSR introduction. If not available, you can briefly summarize the study design, objectives, and population and then you’ll need to craft a novel but brief state-of-the-art analysis based on literature review.

Be sure to align with the key messaging of your study and the indications of your study drug, device, or other type of therapeutic product or method. Utilize good literature review practices, such as choosing peer-reviewed publications, editorials from key opinion leaders in the therapeutic area, and studies with large or randomized cohorts, for support. This section will likely be no longer than one page.

Non-Results Section

Whether to cut and paste the procedures and assessments, primary and secondary endpoints, parameters or hypotheses, planned statistical analyses, monitoring plans, adverse event definitions, and assessment rules directly from the protocol or to simply refer to the protocol and the other study documents in an appendix is a topic of debate amongst medical writers of CSRs. Keep in mind that the CSR should be able to stand alone as a document, and thus while it is important to keep the document concise, it must be comprehensive enough for the reader to understand the study design, objectives, endpoints, processes, and intended analyses without having to refer constantly to the protocol. Regardless, in any summary of the study design, processes, and endpoints, be sure to align with any previously utilized language for consistency across study documents.

Results Section

Using the template and your tables as your structure, summarize the data and pull out any signals and trends, aligning with key messaging where possible. Start with patient disposition and demographics as per the template. Note any protocol deviations that may or may not have impacted patient safety or the evaluation of the outcomes.

Assess and evaluate the study outcome results against primary endpoints and secondary endpoints before discussing any additional secondary outcomes. You should not simply restate the data in the tables; however, refer to specifics in the tables when summarizing.

If you find that you cannot make a statement or conclusion given the TLFs you have, or you are consistently having to perform your own math to support your statements, consider asking your biostatistician to create the tables that will represent the data in a way that will better support your statement. For instance, it is acceptable to state that “most” of the patients responded to the study drug if more than 50% did so; however, if you are having to consistently add up percentages in a table to be able to state, for example, that 77% of the patients responded in a certain way and 33% responded in another, then you should have the biostatistician reformat the data output so it represents the percentages you want to report.

Patient narratives are an important source of context for the reader of the CSR. Depending on your study, you may need to collaborate with either your teammates responsible for assessment of adverse events or the study database administrator to help generate patient and/or event narratives for the CSR. If tasked with compiling or editing patient narratives yourself, the ICH E3 guideline prescribes the necessary components of a comprehensive patient safety narrative (Section 12).{2}

Narrative writing advice has also been previously published and would be a helpful source of direction for the novice narrative writer.{9,10} Narratives are suggested for every patient who experienced a safety endpoint event or death during the course of the study. Tie in patient narratives where appropriate when discussing safety events or refer to the patient narrative section when highlighting a particular patient’s data.

Discussions and Conclusions

Discussion and conclusion sections can either be placed after each section or placed at the end of the document. They should not simply restate the previous table summaries, but provide context and align the results with key messaging. Use an evidence-based approach, including literature references to provide more context as to the nature of the study outcomes with respect to the state of the art for the product/therapy, outcomes from alternate approaches, or further justification of clinical benefit with regard to potential disease progression. The conclusion section at the end of the document is often in bulleted format—not only for ease of the reader, but also to clearly highlight the key messaging and important outcomes you wish to impart.

Executive Summary

The executive summary, while placed at the front of the document prior to the introduction, is often easiest to construct last, as an overall summary of the entire document. The key elements of this summary should briefly recap the study design and objectives. Most likely only the primary and secondary endpoints should be included, unless additional outcomes proved compelling and important within the course of the study. Refer to any important literature comparisons as they relate to any conclusions made about the success or outcomes of the trials. Conclude the executive summary in a similar fashion to the overall study conclusion.

Sidebar: Tips and Tricks for the CSR Writing Process

Review Process

The review process can either facilitate a better document or it can slow down the entire process. The purpose of a cross functional review of a CSR is to confirm accurate key study messaging and data; allow medical review of the patient narratives, outcomes, and conclusionary statements; review the logical flow of ideas; and ensure that the CSR language is consistent across any other study document (i.e., the protocol, statistical analysis plan, etc.).

Sidebar: Tips and Tricks for an Efficient Review Process

CSRs are required by regulatory authorities to report and summarize the outcomes of a clinical study. Pre-project stakeholder determination and timeline planning can help with project management. Templates contained with the ICH E3 guideline can help organize the project as well as help create and finalize a document that is fit for purpose and meets the content expectations of the regulatory reviewer.

• ICH Working Group. 2016. ICH HARMONISED GUIDELINE INTEGRATED ADDENDUM TO ICH E6(R1): GUIDELINE FOR GOOD CLINICAL PRACTICE E6(R2).
• ICH Working Group. 1995. ICH HARMONISED TRIPARTITE GUIDELINE: Structure and Content of Clinical Study Reports E3 .
• U.S. Department of Health and Human Services. 2016. Clinical Trials Registration and Results Information Submission, 42 CFR Part 11. https://www.federalregister.gov/documents/2016/09/21/2016-22129/clinical-trials-registration-and-results-information-submission
• European Commission. 2001. Letter to Stakeholders Regarding the Requirements to provide results for Authortied clinical trials in EUDRACT. In: Article 57(2) Regulation (EC) No 726/2004 and Article 41(2) of Regulation (EC) No 1901/2006. https://eudract.ema.europa.eu/
• U.S. Food and Drug Administration. 2018. ICH Guidance Documents . https://www.fda.gov/science-research/guidance-documents-including-information-sheets-and-notices/ich-guidance-documents
• Transcelerate Biopharma Inc. Clinical Template Suite (CTS), Template, Resources, and Use Guidance. https://transceleratebiopharmainc.com/assets/clinical-content-reuse-assets/
• Hamilton S, Bernstein AB, Blakey G, et al. 2016. Developing the Clarity and Openness in Reporting: E3-based (CORE) Reference user manual for creation of clinical study reports in the era of clinical trial transparency. Research integrity and peer review. 1:4.
• Hamilton S. 2014. Effective authoring of clinical study reports. Medical Writing 23(2).
• Nambiar I. 2018. Analysis of serious adverse event: Writing a narrative. Perspect Clin Res 9(2):103–6.
• Ledade SD, Jain SN, Darji AA, Gupta VH. 2017. Narrative writing: Effective ways and best practices. Perspect Clin Res 8(2):58–62.

Sheryl Stewart, MCR, CCRP, ( [email protected] ) is a Medical Writer working in the medical device industry in southern California.

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How to Write a Medical Research Paper

Last Updated: August 12, 2024 Approved

This article was co-authored by Chris M. Matsko, MD . Dr. Chris M. Matsko is a retired physician based in Pittsburgh, Pennsylvania. With over 25 years of medical research experience, Dr. Matsko was awarded the Pittsburgh Cornell University Leadership Award for Excellence. He holds a BS in Nutritional Science from Cornell University and an MD from the Temple University School of Medicine in 2007. Dr. Matsko earned a Research Writing Certification from the American Medical Writers Association (AMWA) in 2016 and a Medical Writing & Editing Certification from the University of Chicago in 2017. wikiHow marks an article as reader-approved once it receives enough positive feedback. In this case, 89% of readers who voted found the article helpful, earning it our reader-approved status. This article has been viewed 205,622 times.

Writing a medical research paper is similar to writing other research papers in that you want to use reliable sources, write in a clear and organized style, and offer a strong argument for all conclusions you present. In some cases the research you discuss will be data you have actually collected to answer your research questions. Understanding proper formatting, citations, and style will help you write and informative and respected paper.

Researching Your Paper

• Pick something that really interests you to make the research more fun.
• Choose a topic that has unanswered questions and propose solutions.

• Quantitative studies consist of original research performed by the writer. These research papers will need to include sections like Hypothesis (or Research Question), Previous Findings, Method, Limitations, Results, Discussion, and Application.
• Synthesis papers review the research already published and analyze it. They find weaknesses and strengths in the research, apply it to a specific situation, and then indicate a direction for future research.

• Keep track of your sources. Write down all publication information necessary for citation: author, title of article, title of book or journal, publisher, edition, date published, volume number, issue number, page number, and anything else pertaining to your source. A program like Endnote can help you keep track of your sources.
• Take detailed notes as you read. Paraphrase information in your own words or if you copy directly from the article or book, indicate that these are direct quotes by using quotation marks to prevent plagiarism.
• Be sure to keep all of your notes with the correct source.
• Your professor and librarians can also help you find good resources.

• Keep all of your notes in a physical folder or in a digitized form on the computer.
• Start to form the basic outline of your paper using the notes you have collected.

Writing Your Paper

• Start with bullet points and then add in notes you've taken from references that support your ideas. [1] X Trustworthy Source PubMed Central Journal archive from the U.S. National Institutes of Health Go to source
• A common way to format research papers is to follow the IMRAD format. This dictates the structure of your paper in the following order: I ntroduction, M ethods, R esults, a nd D iscussion. [2] X Research source
• The outline is just the basic structure of your paper. Don't worry if you have to rearrange a few times to get it right.
• Ask others to look over your outline and get feedback on the organization.
• Know the audience you are writing for and adjust your style accordingly. [3] X Research source

• Use a standard font type and size, such as Times New Roman 12 point font.
• Double-space your paper.
• If necessary, create a cover page. Most schools require a cover page of some sort. Include your main title, running title (often a shortened version of your main title), author's name, course name, and semester.

• Break up information into sections and subsections and address one main point per section.
• Include any figures or data tables that support your main ideas.
• For a quantitative study, state the methods used to obtain results.

• Clearly state and summarize the main points of your research paper.
• Discuss how this research contributes to the field and why it is important. [4] X Research source
• Highlight potential applications of the theory if appropriate.
• Propose future directions that build upon the research you have presented. [5] X Research source
• Keep the introduction and discussion short, and spend more time explaining the methods and results.

• State why the problem is important to address.
• Discuss what is currently known and what is lacking in the field.
• State the objective of your paper.
• Keep the introduction short.

• Highlight the purpose of the paper and the main conclusions.
• State why your conclusions are important.
• Be concise in your summary of the paper.
• Show that you have a solid study design and a high-quality data set.
• Abstracts are usually one paragraph and between 250 – 500 words.

• Unless otherwise directed, use the American Medical Association (AMA) style guide to properly format citations.
• Add citations at end of a sentence to indicate that you are using someone else's idea. Use these throughout your research paper as needed. They include the author's last name, year of publication, and page number.
• Compile your reference list and add it to the end of your paper.
• Use a citation program if you have access to one to simplify the process.

• Continually revise your paper to make sure it is structured in a logical way.
• Proofread your paper for spelling and grammatical errors.
• Make sure you are following the proper formatting guidelines provided for the paper.
• Have others read your paper to proofread and check for clarity. Revise as needed.

Expert Q&A

• Ask your professor for help if you are stuck or confused about any part of your research paper. They are familiar with the style and structure of papers and can provide you with more resources. Thanks Helpful 0 Not Helpful 0
• Refer to your professor's specific guidelines. Some instructors modify parts of a research paper to better fit their assignment. Others may request supplementary details, such as a synopsis for your research project . Thanks Helpful 0 Not Helpful 0
• Set aside blocks of time specifically for writing each day. Thanks Helpful 0 Not Helpful 0

• Do not plagiarize. Plagiarism is using someone else's work, words, or ideas and presenting them as your own. It is important to cite all sources in your research paper, both through internal citations and on your reference page. Thanks Helpful 4 Not Helpful 2

You Might Also Like

• ↑ http://www.ncbi.nlm.nih.gov/pmc/articles/PMC3178846/
• ↑ http://owl.excelsior.edu/research-and-citations/outlining/outlining-imrad/
• ↑ http://china.elsevier.com/ElsevierDNN/Portals/7/How%20to%20write%20a%20world-class%20paper.pdf
• ↑ http://intqhc.oxfordjournals.org/content/16/3/191
• ↑ http://www.ruf.rice.edu/~bioslabs/tools/report/reportform.html#form

About This Article

To write a medical research paper, research your topic thoroughly and compile your data. Next, organize your notes and create a strong outline that breaks up the information into sections and subsections, addressing one main point per section. Write the results and discussion sections first to go over your findings, then write the introduction to state your objective and provide background information. Finally, write the abstract, which concisely summarizes the article by highlighting the main points. For tips on formatting and using citations, read on! Did this summary help you? Yes No

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How To Write A Lab Report | Step-by-Step Guide & Examples

Published on May 20, 2021 by Pritha Bhandari . Revised on July 23, 2023.

A lab report conveys the aim, methods, results, and conclusions of a scientific experiment. The main purpose of a lab report is to demonstrate your understanding of the scientific method by performing and evaluating a hands-on lab experiment. This type of assignment is usually shorter than a research paper .

Lab reports are commonly used in science, technology, engineering, and mathematics (STEM) fields. This article focuses on how to structure and write a lab report.

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Table of contents

Structuring a lab report, introduction, other interesting articles, frequently asked questions about lab reports.

The sections of a lab report can vary between scientific fields and course requirements, but they usually contain the purpose, methods, and findings of a lab experiment .

Each section of a lab report has its own purpose.

• Title: expresses the topic of your study
• Abstract : summarizes your research aims, methods, results, and conclusions
• Introduction: establishes the context needed to understand the topic
• Method: describes the materials and procedures used in the experiment
• Results: reports all descriptive and inferential statistical analyses
• Discussion: interprets and evaluates results and identifies limitations
• Conclusion: sums up the main findings of your experiment
• References: list of all sources cited using a specific style (e.g. APA )
• Appendices : contains lengthy materials, procedures, tables or figures

Although most lab reports contain these sections, some sections can be omitted or combined with others. For example, some lab reports contain a brief section on research aims instead of an introduction, and a separate conclusion is not always required.

If you’re not sure, it’s best to check your lab report requirements with your instructor.

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Your title provides the first impression of your lab report – effective titles communicate the topic and/or the findings of your study in specific terms.

Create a title that directly conveys the main focus or purpose of your study. It doesn’t need to be creative or thought-provoking, but it should be informative.

• The effects of varying nitrogen levels on tomato plant height.
• Testing the universality of the McGurk effect.
• Comparing the viscosity of common liquids found in kitchens.

An abstract condenses a lab report into a brief overview of about 150–300 words. It should provide readers with a compact version of the research aims, the methods and materials used, the main results, and the final conclusion.

Think of it as a way of giving readers a preview of your full lab report. Write the abstract last, in the past tense, after you’ve drafted all the other sections of your report, so you’ll be able to succinctly summarize each section.

To write a lab report abstract, use these guiding questions:

• What is the wider context of your study?
• What research question were you trying to answer?
• How did you perform the experiment?
• What did your results show?
• How did you interpret your results?
• What is the importance of your findings?

Nitrogen is a necessary nutrient for high quality plants. Tomatoes, one of the most consumed fruits worldwide, rely on nitrogen for healthy leaves and stems to grow fruit. This experiment tested whether nitrogen levels affected tomato plant height in a controlled setting. It was expected that higher levels of nitrogen fertilizer would yield taller tomato plants.

Levels of nitrogen fertilizer were varied between three groups of tomato plants. The control group did not receive any nitrogen fertilizer, while one experimental group received low levels of nitrogen fertilizer, and a second experimental group received high levels of nitrogen fertilizer. All plants were grown from seeds, and heights were measured 50 days into the experiment.

The effects of nitrogen levels on plant height were tested between groups using an ANOVA. The plants with the highest level of nitrogen fertilizer were the tallest, while the plants with low levels of nitrogen exceeded the control group plants in height. In line with expectations and previous findings, the effects of nitrogen levels on plant height were statistically significant. This study strengthens the importance of nitrogen for tomato plants.

Your lab report introduction should set the scene for your experiment. One way to write your introduction is with a funnel (an inverted triangle) structure:

• Start with the broad, general research topic
• Narrow your topic down your specific study focus
• End with a clear research question

Begin by providing background information on your research topic and explaining why it’s important in a broad real-world or theoretical context. Describe relevant previous research on your topic and note how your study may confirm it or expand it, or fill a gap in the research field.

This lab experiment builds on previous research from Haque, Paul, and Sarker (2011), who demonstrated that tomato plant yield increased at higher levels of nitrogen. However, the present research focuses on plant height as a growth indicator and uses a lab-controlled setting instead.

Next, go into detail on the theoretical basis for your study and describe any directly relevant laws or equations that you’ll be using. State your main research aims and expectations by outlining your hypotheses .

Based on the importance of nitrogen for tomato plants, the primary hypothesis was that the plants with the high levels of nitrogen would grow the tallest. The secondary hypothesis was that plants with low levels of nitrogen would grow taller than plants with no nitrogen.

Your introduction doesn’t need to be long, but you may need to organize it into a few paragraphs or with subheadings such as “Research Context” or “Research Aims.”

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A lab report Method section details the steps you took to gather and analyze data. Give enough detail so that others can follow or evaluate your procedures. Write this section in the past tense. If you need to include any long lists of procedural steps or materials, place them in the Appendices section but refer to them in the text here.

You should describe your experimental design, your subjects, materials, and specific procedures used for data collection and analysis.

Experimental design

Briefly note whether your experiment is a within-subjects  or between-subjects design, and describe how your sample units were assigned to conditions if relevant.

A between-subjects design with three groups of tomato plants was used. The control group did not receive any nitrogen fertilizer. The first experimental group received a low level of nitrogen fertilizer, while the second experimental group received a high level of nitrogen fertilizer.

Describe human subjects in terms of demographic characteristics, and animal or plant subjects in terms of genetic background. Note the total number of subjects as well as the number of subjects per condition or per group. You should also state how you recruited subjects for your study.

List the equipment or materials you used to gather data and state the model names for any specialized equipment.

List of materials

35 Tomato seeds

15 plant pots (15 cm tall)

Light lamps (50,000 lux)

Nitrogen fertilizer

Measuring tape

Describe your experimental settings and conditions in detail. You can provide labelled diagrams or images of the exact set-up necessary for experimental equipment. State how extraneous variables were controlled through restriction or by fixing them at a certain level (e.g., keeping the lab at room temperature).

Light levels were fixed throughout the experiment, and the plants were exposed to 12 hours of light a day. Temperature was restricted to between 23 and 25℃. The pH and carbon levels of the soil were also held constant throughout the experiment as these variables could influence plant height. The plants were grown in rooms free of insects or other pests, and they were spaced out adequately.

Your experimental procedure should describe the exact steps you took to gather data in chronological order. You’ll need to provide enough information so that someone else can replicate your procedure, but you should also be concise. Place detailed information in the appendices where appropriate.

In a lab experiment, you’ll often closely follow a lab manual to gather data. Some instructors will allow you to simply reference the manual and state whether you changed any steps based on practical considerations. Other instructors may want you to rewrite the lab manual procedures as complete sentences in coherent paragraphs, while noting any changes to the steps that you applied in practice.

If you’re performing extensive data analysis, be sure to state your planned analysis methods as well. This includes the types of tests you’ll perform and any programs or software you’ll use for calculations (if relevant).

First, tomato seeds were sown in wooden flats containing soil about 2 cm below the surface. Each seed was kept 3-5 cm apart. The flats were covered to keep the soil moist until germination. The seedlings were removed and transplanted to pots 8 days later, with a maximum of 2 plants to a pot. Each pot was watered once a day to keep the soil moist.

The nitrogen fertilizer treatment was applied to the plant pots 12 days after transplantation. The control group received no treatment, while the first experimental group received a low concentration, and the second experimental group received a high concentration. There were 5 pots in each group, and each plant pot was labelled to indicate the group the plants belonged to.

50 days after the start of the experiment, plant height was measured for all plants. A measuring tape was used to record the length of the plant from ground level to the top of the tallest leaf.

In your results section, you should report the results of any statistical analysis procedures that you undertook. You should clearly state how the results of statistical tests support or refute your initial hypotheses.

The main results to report include:

• any descriptive statistics
• statistical test results
• the significance of the test results
• estimates of standard error or confidence intervals

The mean heights of the plants in the control group, low nitrogen group, and high nitrogen groups were 20.3, 25.1, and 29.6 cm respectively. A one-way ANOVA was applied to calculate the effect of nitrogen fertilizer level on plant height. The results demonstrated statistically significant ( p = .03) height differences between groups.

Next, post-hoc tests were performed to assess the primary and secondary hypotheses. In support of the primary hypothesis, the high nitrogen group plants were significantly taller than the low nitrogen group and the control group plants. Similarly, the results supported the secondary hypothesis: the low nitrogen plants were taller than the control group plants.

These results can be reported in the text or in tables and figures. Use text for highlighting a few key results, but present large sets of numbers in tables, or show relationships between variables with graphs.

You should also include sample calculations in the Results section for complex experiments. For each sample calculation, provide a brief description of what it does and use clear symbols. Present your raw data in the Appendices section and refer to it to highlight any outliers or trends.

The Discussion section will help demonstrate your understanding of the experimental process and your critical thinking skills.

In this section, you can:

• Interpret your results
• Compare your findings with your expectations
• Identify any sources of experimental error
• Explain any unexpected results
• Suggest possible improvements for further studies

Interpreting your results involves clarifying how your results help you answer your main research question. Report whether your results support your hypotheses.

• Did you measure what you sought out to measure?
• Were your analysis procedures appropriate for this type of data?

Compare your findings with other research and explain any key differences in findings.

• Are your results in line with those from previous studies or your classmates’ results? Why or why not?

An effective Discussion section will also highlight the strengths and limitations of a study.

• Did you have high internal validity or reliability?
• How did you establish these aspects of your study?

When describing limitations, use specific examples. For example, if random error contributed substantially to the measurements in your study, state the particular sources of error (e.g., imprecise apparatus) and explain ways to improve them.

The results support the hypothesis that nitrogen levels affect plant height, with increasing levels producing taller plants. These statistically significant results are taken together with previous research to support the importance of nitrogen as a nutrient for tomato plant growth.

However, unlike previous studies, this study focused on plant height as an indicator of plant growth in the present experiment. Importantly, plant height may not always reflect plant health or fruit yield, so measuring other indicators would have strengthened the study findings.

Another limitation of the study is the plant height measurement technique, as the measuring tape was not suitable for plants with extreme curvature. Future studies may focus on measuring plant height in different ways.

The main strengths of this study were the controls for extraneous variables, such as pH and carbon levels of the soil. All other factors that could affect plant height were tightly controlled to isolate the effects of nitrogen levels, resulting in high internal validity for this study.

Your conclusion should be the final section of your lab report. Here, you’ll summarize the findings of your experiment, with a brief overview of the strengths and limitations, and implications of your study for further research.

Some lab reports may omit a Conclusion section because it overlaps with the Discussion section, but you should check with your instructor before doing so.

If you want to know more about AI for academic writing, AI tools, or fallacies make sure to check out some of our other articles with explanations and examples or go directly to our tools!

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A lab report conveys the aim, methods, results, and conclusions of a scientific experiment . Lab reports are commonly assigned in science, technology, engineering, and mathematics (STEM) fields.

The purpose of a lab report is to demonstrate your understanding of the scientific method with a hands-on lab experiment. Course instructors will often provide you with an experimental design and procedure. Your task is to write up how you actually performed the experiment and evaluate the outcome.

In contrast, a research paper requires you to independently develop an original argument. It involves more in-depth research and interpretation of sources and data.

A lab report is usually shorter than a research paper.

The sections of a lab report can vary between scientific fields and course requirements, but it usually contains the following:

• Abstract: summarizes your research aims, methods, results, and conclusions
• References: list of all sources cited using a specific style (e.g. APA)
• Appendices: contains lengthy materials, procedures, tables or figures

The results chapter or section simply and objectively reports what you found, without speculating on why you found these results. The discussion interprets the meaning of the results, puts them in context, and explains why they matter.

In qualitative research , results and discussion are sometimes combined. But in quantitative research , it’s considered important to separate the objective results from your interpretation of them.

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Scientific Reports

What this handout is about.

This handout provides a general guide to writing reports about scientific research you’ve performed. In addition to describing the conventional rules about the format and content of a lab report, we’ll also attempt to convey why these rules exist, so you’ll get a clearer, more dependable idea of how to approach this writing situation. Readers of this handout may also find our handout on writing in the sciences useful.

Background and pre-writing

Why do we write research reports.

You did an experiment or study for your science class, and now you have to write it up for your teacher to review. You feel that you understood the background sufficiently, designed and completed the study effectively, obtained useful data, and can use those data to draw conclusions about a scientific process or principle. But how exactly do you write all that? What is your teacher expecting to see?

To take some of the guesswork out of answering these questions, try to think beyond the classroom setting. In fact, you and your teacher are both part of a scientific community, and the people who participate in this community tend to share the same values. As long as you understand and respect these values, your writing will likely meet the expectations of your audience—including your teacher.

So why are you writing this research report? The practical answer is “Because the teacher assigned it,” but that’s classroom thinking. Generally speaking, people investigating some scientific hypothesis have a responsibility to the rest of the scientific world to report their findings, particularly if these findings add to or contradict previous ideas. The people reading such reports have two primary goals:

• They want to gather the information presented.
• They want to know that the findings are legitimate.

Your job as a writer, then, is to fulfill these two goals.

How do I do that?

Good question. Here is the basic format scientists have designed for research reports:

• Introduction

Methods and Materials

This format, sometimes called “IMRAD,” may take slightly different shapes depending on the discipline or audience; some ask you to include an abstract or separate section for the hypothesis, or call the Discussion section “Conclusions,” or change the order of the sections (some professional and academic journals require the Methods section to appear last). Overall, however, the IMRAD format was devised to represent a textual version of the scientific method.

The scientific method, you’ll probably recall, involves developing a hypothesis, testing it, and deciding whether your findings support the hypothesis. In essence, the format for a research report in the sciences mirrors the scientific method but fleshes out the process a little. Below, you’ll find a table that shows how each written section fits into the scientific method and what additional information it offers the reader.

Thinking of your research report as based on the scientific method, but elaborated in the ways described above, may help you to meet your audience’s expectations successfully. We’re going to proceed by explicitly connecting each section of the lab report to the scientific method, then explaining why and how you need to elaborate that section.

Although this handout takes each section in the order in which it should be presented in the final report, you may for practical reasons decide to compose sections in another order. For example, many writers find that composing their Methods and Results before the other sections helps to clarify their idea of the experiment or study as a whole. You might consider using each assignment to practice different approaches to drafting the report, to find the order that works best for you.

What should I do before drafting the lab report?

The best way to prepare to write the lab report is to make sure that you fully understand everything you need to about the experiment. Obviously, if you don’t quite know what went on during the lab, you’re going to find it difficult to explain the lab satisfactorily to someone else. To make sure you know enough to write the report, complete the following steps:

• What are we going to do in this lab? (That is, what’s the procedure?)
• Why are we going to do it that way?
• What are we hoping to learn from this experiment?
• Why would we benefit from this knowledge?
• Consult your lab supervisor as you perform the lab. If you don’t know how to answer one of the questions above, for example, your lab supervisor will probably be able to explain it to you (or, at least, help you figure it out).
• Plan the steps of the experiment carefully with your lab partners. The less you rush, the more likely it is that you’ll perform the experiment correctly and record your findings accurately. Also, take some time to think about the best way to organize the data before you have to start putting numbers down. If you can design a table to account for the data, that will tend to work much better than jotting results down hurriedly on a scrap piece of paper.
• Record the data carefully so you get them right. You won’t be able to trust your conclusions if you have the wrong data, and your readers will know you messed up if the other three people in your group have “97 degrees” and you have “87.”
• Consult with your lab partners about everything you do. Lab groups often make one of two mistakes: two people do all the work while two have a nice chat, or everybody works together until the group finishes gathering the raw data, then scrams outta there. Collaborate with your partners, even when the experiment is “over.” What trends did you observe? Was the hypothesis supported? Did you all get the same results? What kind of figure should you use to represent your findings? The whole group can work together to answer these questions.
• Consider your audience. You may believe that audience is a non-issue: it’s your lab TA, right? Well, yes—but again, think beyond the classroom. If you write with only your lab instructor in mind, you may omit material that is crucial to a complete understanding of your experiment, because you assume the instructor knows all that stuff already. As a result, you may receive a lower grade, since your TA won’t be sure that you understand all the principles at work. Try to write towards a student in the same course but a different lab section. That student will have a fair degree of scientific expertise but won’t know much about your experiment particularly. Alternatively, you could envision yourself five years from now, after the reading and lectures for this course have faded a bit. What would you remember, and what would you need explained more clearly (as a refresher)?

Once you’ve completed these steps as you perform the experiment, you’ll be in a good position to draft an effective lab report.

Introductions

How do i write a strong introduction.

For the purposes of this handout, we’ll consider the Introduction to contain four basic elements: the purpose, the scientific literature relevant to the subject, the hypothesis, and the reasons you believed your hypothesis viable. Let’s start by going through each element of the Introduction to clarify what it covers and why it’s important. Then we can formulate a logical organizational strategy for the section.

The inclusion of the purpose (sometimes called the objective) of the experiment often confuses writers. The biggest misconception is that the purpose is the same as the hypothesis. Not quite. We’ll get to hypotheses in a minute, but basically they provide some indication of what you expect the experiment to show. The purpose is broader, and deals more with what you expect to gain through the experiment. In a professional setting, the hypothesis might have something to do with how cells react to a certain kind of genetic manipulation, but the purpose of the experiment is to learn more about potential cancer treatments. Undergraduate reports don’t often have this wide-ranging a goal, but you should still try to maintain the distinction between your hypothesis and your purpose. In a solubility experiment, for example, your hypothesis might talk about the relationship between temperature and the rate of solubility, but the purpose is probably to learn more about some specific scientific principle underlying the process of solubility.

For starters, most people say that you should write out your working hypothesis before you perform the experiment or study. Many beginning science students neglect to do so and find themselves struggling to remember precisely which variables were involved in the process or in what way the researchers felt that they were related. Write your hypothesis down as you develop it—you’ll be glad you did.

As for the form a hypothesis should take, it’s best not to be too fancy or complicated; an inventive style isn’t nearly so important as clarity here. There’s nothing wrong with beginning your hypothesis with the phrase, “It was hypothesized that . . .” Be as specific as you can about the relationship between the different objects of your study. In other words, explain that when term A changes, term B changes in this particular way. Readers of scientific writing are rarely content with the idea that a relationship between two terms exists—they want to know what that relationship entails.

Not a hypothesis:

“It was hypothesized that there is a significant relationship between the temperature of a solvent and the rate at which a solute dissolves.”

Hypothesis:

“It was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases.”

Put more technically, most hypotheses contain both an independent and a dependent variable. The independent variable is what you manipulate to test the reaction; the dependent variable is what changes as a result of your manipulation. In the example above, the independent variable is the temperature of the solvent, and the dependent variable is the rate of solubility. Be sure that your hypothesis includes both variables.

Justify your hypothesis

You need to do more than tell your readers what your hypothesis is; you also need to assure them that this hypothesis was reasonable, given the circumstances. In other words, use the Introduction to explain that you didn’t just pluck your hypothesis out of thin air. (If you did pluck it out of thin air, your problems with your report will probably extend beyond using the appropriate format.) If you posit that a particular relationship exists between the independent and the dependent variable, what led you to believe your “guess” might be supported by evidence?

Scientists often refer to this type of justification as “motivating” the hypothesis, in the sense that something propelled them to make that prediction. Often, motivation includes what we already know—or rather, what scientists generally accept as true (see “Background/previous research” below). But you can also motivate your hypothesis by relying on logic or on your own observations. If you’re trying to decide which solutes will dissolve more rapidly in a solvent at increased temperatures, you might remember that some solids are meant to dissolve in hot water (e.g., bouillon cubes) and some are used for a function precisely because they withstand higher temperatures (they make saucepans out of something). Or you can think about whether you’ve noticed sugar dissolving more rapidly in your glass of iced tea or in your cup of coffee. Even such basic, outside-the-lab observations can help you justify your hypothesis as reasonable.

Background/previous research

This part of the Introduction demonstrates to the reader your awareness of how you’re building on other scientists’ work. If you think of the scientific community as engaging in a series of conversations about various topics, then you’ll recognize that the relevant background material will alert the reader to which conversation you want to enter.

Generally speaking, authors writing journal articles use the background for slightly different purposes than do students completing assignments. Because readers of academic journals tend to be professionals in the field, authors explain the background in order to permit readers to evaluate the study’s pertinence for their own work. You, on the other hand, write toward a much narrower audience—your peers in the course or your lab instructor—and so you must demonstrate that you understand the context for the (presumably assigned) experiment or study you’ve completed. For example, if your professor has been talking about polarity during lectures, and you’re doing a solubility experiment, you might try to connect the polarity of a solid to its relative solubility in certain solvents. In any event, both professional researchers and undergraduates need to connect the background material overtly to their own work.

Organization of this section

Most of the time, writers begin by stating the purpose or objectives of their own work, which establishes for the reader’s benefit the “nature and scope of the problem investigated” (Day 1994). Once you have expressed your purpose, you should then find it easier to move from the general purpose, to relevant material on the subject, to your hypothesis. In abbreviated form, an Introduction section might look like this:

“The purpose of the experiment was to test conventional ideas about solubility in the laboratory [purpose] . . . According to Whitecoat and Labrat (1999), at higher temperatures the molecules of solvents move more quickly . . . We know from the class lecture that molecules moving at higher rates of speed collide with one another more often and thus break down more easily [background material/motivation] . . . Thus, it was hypothesized that as the temperature of a solvent increases, the rate at which a solute will dissolve in that solvent increases [hypothesis].”

Again—these are guidelines, not commandments. Some writers and readers prefer different structures for the Introduction. The one above merely illustrates a common approach to organizing material.

How do I write a strong Materials and Methods section?

As with any piece of writing, your Methods section will succeed only if it fulfills its readers’ expectations, so you need to be clear in your own mind about the purpose of this section. Let’s review the purpose as we described it above: in this section, you want to describe in detail how you tested the hypothesis you developed and also to clarify the rationale for your procedure. In science, it’s not sufficient merely to design and carry out an experiment. Ultimately, others must be able to verify your findings, so your experiment must be reproducible, to the extent that other researchers can follow the same procedure and obtain the same (or similar) results.

Here’s a real-world example of the importance of reproducibility. In 1989, physicists Stanley Pons and Martin Fleischman announced that they had discovered “cold fusion,” a way of producing excess heat and power without the nuclear radiation that accompanies “hot fusion.” Such a discovery could have great ramifications for the industrial production of energy, so these findings created a great deal of interest. When other scientists tried to duplicate the experiment, however, they didn’t achieve the same results, and as a result many wrote off the conclusions as unjustified (or worse, a hoax). To this day, the viability of cold fusion is debated within the scientific community, even though an increasing number of researchers believe it possible. So when you write your Methods section, keep in mind that you need to describe your experiment well enough to allow others to replicate it exactly.

With these goals in mind, let’s consider how to write an effective Methods section in terms of content, structure, and style.

Sometimes the hardest thing about writing this section isn’t what you should talk about, but what you shouldn’t talk about. Writers often want to include the results of their experiment, because they measured and recorded the results during the course of the experiment. But such data should be reserved for the Results section. In the Methods section, you can write that you recorded the results, or how you recorded the results (e.g., in a table), but you shouldn’t write what the results were—not yet. Here, you’re merely stating exactly how you went about testing your hypothesis. As you draft your Methods section, ask yourself the following questions:

• How much detail? Be precise in providing details, but stay relevant. Ask yourself, “Would it make any difference if this piece were a different size or made from a different material?” If not, you probably don’t need to get too specific. If so, you should give as many details as necessary to prevent this experiment from going awry if someone else tries to carry it out. Probably the most crucial detail is measurement; you should always quantify anything you can, such as time elapsed, temperature, mass, volume, etc.
• Rationale: Be sure that as you’re relating your actions during the experiment, you explain your rationale for the protocol you developed. If you capped a test tube immediately after adding a solute to a solvent, why did you do that? (That’s really two questions: why did you cap it, and why did you cap it immediately?) In a professional setting, writers provide their rationale as a way to explain their thinking to potential critics. On one hand, of course, that’s your motivation for talking about protocol, too. On the other hand, since in practical terms you’re also writing to your teacher (who’s seeking to evaluate how well you comprehend the principles of the experiment), explaining the rationale indicates that you understand the reasons for conducting the experiment in that way, and that you’re not just following orders. Critical thinking is crucial—robots don’t make good scientists.
• Control: Most experiments will include a control, which is a means of comparing experimental results. (Sometimes you’ll need to have more than one control, depending on the number of hypotheses you want to test.) The control is exactly the same as the other items you’re testing, except that you don’t manipulate the independent variable-the condition you’re altering to check the effect on the dependent variable. For example, if you’re testing solubility rates at increased temperatures, your control would be a solution that you didn’t heat at all; that way, you’ll see how quickly the solute dissolves “naturally” (i.e., without manipulation), and you’ll have a point of reference against which to compare the solutions you did heat.

Describe the control in the Methods section. Two things are especially important in writing about the control: identify the control as a control, and explain what you’re controlling for. Here is an example:

“As a control for the temperature change, we placed the same amount of solute in the same amount of solvent, and let the solution stand for five minutes without heating it.”

Structure and style

Organization is especially important in the Methods section of a lab report because readers must understand your experimental procedure completely. Many writers are surprised by the difficulty of conveying what they did during the experiment, since after all they’re only reporting an event, but it’s often tricky to present this information in a coherent way. There’s a fairly standard structure you can use to guide you, and following the conventions for style can help clarify your points.

• Subsections: Occasionally, researchers use subsections to report their procedure when the following circumstances apply: 1) if they’ve used a great many materials; 2) if the procedure is unusually complicated; 3) if they’ve developed a procedure that won’t be familiar to many of their readers. Because these conditions rarely apply to the experiments you’ll perform in class, most undergraduate lab reports won’t require you to use subsections. In fact, many guides to writing lab reports suggest that you try to limit your Methods section to a single paragraph.
• Narrative structure: Think of this section as telling a story about a group of people and the experiment they performed. Describe what you did in the order in which you did it. You may have heard the old joke centered on the line, “Disconnect the red wire, but only after disconnecting the green wire,” where the person reading the directions blows everything to kingdom come because the directions weren’t in order. We’re used to reading about events chronologically, and so your readers will generally understand what you did if you present that information in the same way. Also, since the Methods section does generally appear as a narrative (story), you want to avoid the “recipe” approach: “First, take a clean, dry 100 ml test tube from the rack. Next, add 50 ml of distilled water.” You should be reporting what did happen, not telling the reader how to perform the experiment: “50 ml of distilled water was poured into a clean, dry 100 ml test tube.” Hint: most of the time, the recipe approach comes from copying down the steps of the procedure from your lab manual, so you may want to draft the Methods section initially without consulting your manual. Later, of course, you can go back and fill in any part of the procedure you inadvertently overlooked.
• Past tense: Remember that you’re describing what happened, so you should use past tense to refer to everything you did during the experiment. Writers are often tempted to use the imperative (“Add 5 g of the solid to the solution”) because that’s how their lab manuals are worded; less frequently, they use present tense (“5 g of the solid are added to the solution”). Instead, remember that you’re talking about an event which happened at a particular time in the past, and which has already ended by the time you start writing, so simple past tense will be appropriate in this section (“5 g of the solid were added to the solution” or “We added 5 g of the solid to the solution”).
• Active: We heated the solution to 80°C. (The subject, “we,” performs the action, heating.)
• Passive: The solution was heated to 80°C. (The subject, “solution,” doesn’t do the heating–it is acted upon, not acting.)

Increasingly, especially in the social sciences, using first person and active voice is acceptable in scientific reports. Most readers find that this style of writing conveys information more clearly and concisely. This rhetorical choice thus brings two scientific values into conflict: objectivity versus clarity. Since the scientific community hasn’t reached a consensus about which style it prefers, you may want to ask your lab instructor.

How do I write a strong Results section?

Here’s a paradox for you. The Results section is often both the shortest (yay!) and most important (uh-oh!) part of your report. Your Materials and Methods section shows how you obtained the results, and your Discussion section explores the significance of the results, so clearly the Results section forms the backbone of the lab report. This section provides the most critical information about your experiment: the data that allow you to discuss how your hypothesis was or wasn’t supported. But it doesn’t provide anything else, which explains why this section is generally shorter than the others.

Before you write this section, look at all the data you collected to figure out what relates significantly to your hypothesis. You’ll want to highlight this material in your Results section. Resist the urge to include every bit of data you collected, since perhaps not all are relevant. Also, don’t try to draw conclusions about the results—save them for the Discussion section. In this section, you’re reporting facts. Nothing your readers can dispute should appear in the Results section.

Most Results sections feature three distinct parts: text, tables, and figures. Let’s consider each part one at a time.

This should be a short paragraph, generally just a few lines, that describes the results you obtained from your experiment. In a relatively simple experiment, one that doesn’t produce a lot of data for you to repeat, the text can represent the entire Results section. Don’t feel that you need to include lots of extraneous detail to compensate for a short (but effective) text; your readers appreciate discrimination more than your ability to recite facts. In a more complex experiment, you may want to use tables and/or figures to help guide your readers toward the most important information you gathered. In that event, you’ll need to refer to each table or figure directly, where appropriate:

“Table 1 lists the rates of solubility for each substance”

“Solubility increased as the temperature of the solution increased (see Figure 1).”

If you do use tables or figures, make sure that you don’t present the same material in both the text and the tables/figures, since in essence you’ll just repeat yourself, probably annoying your readers with the redundancy of your statements.

Feel free to describe trends that emerge as you examine the data. Although identifying trends requires some judgment on your part and so may not feel like factual reporting, no one can deny that these trends do exist, and so they properly belong in the Results section. Example:

“Heating the solution increased the rate of solubility of polar solids by 45% but had no effect on the rate of solubility in solutions containing non-polar solids.”

This point isn’t debatable—you’re just pointing out what the data show.

As in the Materials and Methods section, you want to refer to your data in the past tense, because the events you recorded have already occurred and have finished occurring. In the example above, note the use of “increased” and “had,” rather than “increases” and “has.” (You don’t know from your experiment that heating always increases the solubility of polar solids, but it did that time.)

You shouldn’t put information in the table that also appears in the text. You also shouldn’t use a table to present irrelevant data, just to show you did collect these data during the experiment. Tables are good for some purposes and situations, but not others, so whether and how you’ll use tables depends upon what you need them to accomplish.

Tables are useful ways to show variation in data, but not to present a great deal of unchanging measurements. If you’re dealing with a scientific phenomenon that occurs only within a certain range of temperatures, for example, you don’t need to use a table to show that the phenomenon didn’t occur at any of the other temperatures. How useful is this table?

As you can probably see, no solubility was observed until the trial temperature reached 50°C, a fact that the text part of the Results section could easily convey. The table could then be limited to what happened at 50°C and higher, thus better illustrating the differences in solubility rates when solubility did occur.

As a rule, try not to use a table to describe any experimental event you can cover in one sentence of text. Here’s an example of an unnecessary table from How to Write and Publish a Scientific Paper , by Robert A. Day:

As Day notes, all the information in this table can be summarized in one sentence: “S. griseus, S. coelicolor, S. everycolor, and S. rainbowenski grew under aerobic conditions, whereas S. nocolor and S. greenicus required anaerobic conditions.” Most readers won’t find the table clearer than that one sentence.

When you do have reason to tabulate material, pay attention to the clarity and readability of the format you use. Here are a few tips:

• Number your table. Then, when you refer to the table in the text, use that number to tell your readers which table they can review to clarify the material.
• Give your table a title. This title should be descriptive enough to communicate the contents of the table, but not so long that it becomes difficult to follow. The titles in the sample tables above are acceptable.
• Arrange your table so that readers read vertically, not horizontally. For the most part, this rule means that you should construct your table so that like elements read down, not across. Think about what you want your readers to compare, and put that information in the column (up and down) rather than in the row (across). Usually, the point of comparison will be the numerical data you collect, so especially make sure you have columns of numbers, not rows.Here’s an example of how drastically this decision affects the readability of your table (from A Short Guide to Writing about Chemistry , by Herbert Beall and John Trimbur). Look at this table, which presents the relevant data in horizontal rows:

It’s a little tough to see the trends that the author presumably wants to present in this table. Compare this table, in which the data appear vertically:

The second table shows how putting like elements in a vertical column makes for easier reading. In this case, the like elements are the measurements of length and height, over five trials–not, as in the first table, the length and height measurements for each trial.

• Make sure to include units of measurement in the tables. Readers might be able to guess that you measured something in millimeters, but don’t make them try.

• Don’t use vertical lines as part of the format for your table. This convention exists because journals prefer not to have to reproduce these lines because the tables then become more expensive to print. Even though it’s fairly unlikely that you’ll be sending your Biology 11 lab report to Science for publication, your readers still have this expectation. Consequently, if you use the table-drawing option in your word-processing software, choose the option that doesn’t rely on a “grid” format (which includes vertical lines).

How do I include figures in my report?

Although tables can be useful ways of showing trends in the results you obtained, figures (i.e., illustrations) can do an even better job of emphasizing such trends. Lab report writers often use graphic representations of the data they collected to provide their readers with a literal picture of how the experiment went.

When should you use a figure?

Remember the circumstances under which you don’t need a table: when you don’t have a great deal of data or when the data you have don’t vary a lot. Under the same conditions, you would probably forgo the figure as well, since the figure would be unlikely to provide your readers with an additional perspective. Scientists really don’t like their time wasted, so they tend not to respond favorably to redundancy.

If you’re trying to decide between using a table and creating a figure to present your material, consider the following a rule of thumb. The strength of a table lies in its ability to supply large amounts of exact data, whereas the strength of a figure is its dramatic illustration of important trends within the experiment. If you feel that your readers won’t get the full impact of the results you obtained just by looking at the numbers, then a figure might be appropriate.

Of course, an undergraduate class may expect you to create a figure for your lab experiment, if only to make sure that you can do so effectively. If this is the case, then don’t worry about whether to use figures or not—concentrate instead on how best to accomplish your task.

Figures can include maps, photographs, pen-and-ink drawings, flow charts, bar graphs, and section graphs (“pie charts”). But the most common figure by far, especially for undergraduates, is the line graph, so we’ll focus on that type in this handout.

At the undergraduate level, you can often draw and label your graphs by hand, provided that the result is clear, legible, and drawn to scale. Computer technology has, however, made creating line graphs a lot easier. Most word-processing software has a number of functions for transferring data into graph form; many scientists have found Microsoft Excel, for example, a helpful tool in graphing results. If you plan on pursuing a career in the sciences, it may be well worth your while to learn to use a similar program.

Computers can’t, however, decide for you how your graph really works; you have to know how to design your graph to meet your readers’ expectations. Here are some of these expectations:

• Keep it as simple as possible. You may be tempted to signal the complexity of the information you gathered by trying to design a graph that accounts for that complexity. But remember the purpose of your graph: to dramatize your results in a manner that’s easy to see and grasp. Try not to make the reader stare at the graph for a half hour to find the important line among the mass of other lines. For maximum effectiveness, limit yourself to three to five lines per graph; if you have more data to demonstrate, use a set of graphs to account for it, rather than trying to cram it all into a single figure.
• Plot the independent variable on the horizontal (x) axis and the dependent variable on the vertical (y) axis. Remember that the independent variable is the condition that you manipulated during the experiment and the dependent variable is the condition that you measured to see if it changed along with the independent variable. Placing the variables along their respective axes is mostly just a convention, but since your readers are accustomed to viewing graphs in this way, you’re better off not challenging the convention in your report.
• Label each axis carefully, and be especially careful to include units of measure. You need to make sure that your readers understand perfectly well what your graph indicates.
• Number and title your graphs. As with tables, the title of the graph should be informative but concise, and you should refer to your graph by number in the text (e.g., “Figure 1 shows the increase in the solubility rate as a function of temperature”).
• Many editors of professional scientific journals prefer that writers distinguish the lines in their graphs by attaching a symbol to them, usually a geometric shape (triangle, square, etc.), and using that symbol throughout the curve of the line. Generally, readers have a hard time distinguishing dotted lines from dot-dash lines from straight lines, so you should consider staying away from this system. Editors don’t usually like different-colored lines within a graph because colors are difficult and expensive to reproduce; colors may, however, be great for your purposes, as long as you’re not planning to submit your paper to Nature. Use your discretion—try to employ whichever technique dramatizes the results most effectively.
• Try to gather data at regular intervals, so the plot points on your graph aren’t too far apart. You can’t be sure of the arc you should draw between the plot points if the points are located at the far corners of the graph; over a fifteen-minute interval, perhaps the change occurred in the first or last thirty seconds of that period (in which case your straight-line connection between the points is misleading).
• If you’re worried that you didn’t collect data at sufficiently regular intervals during your experiment, go ahead and connect the points with a straight line, but you may want to examine this problem as part of your Discussion section.
• Make your graph large enough so that everything is legible and clearly demarcated, but not so large that it either overwhelms the rest of the Results section or provides a far greater range than you need to illustrate your point. If, for example, the seedlings of your plant grew only 15 mm during the trial, you don’t need to construct a graph that accounts for 100 mm of growth. The lines in your graph should more or less fill the space created by the axes; if you see that your data is confined to the lower left portion of the graph, you should probably re-adjust your scale.
• If you create a set of graphs, make them the same size and format, including all the verbal and visual codes (captions, symbols, scale, etc.). You want to be as consistent as possible in your illustrations, so that your readers can easily make the comparisons you’re trying to get them to see.

How do I write a strong Discussion section?

The discussion section is probably the least formalized part of the report, in that you can’t really apply the same structure to every type of experiment. In simple terms, here you tell your readers what to make of the Results you obtained. If you have done the Results part well, your readers should already recognize the trends in the data and have a fairly clear idea of whether your hypothesis was supported. Because the Results can seem so self-explanatory, many students find it difficult to know what material to add in this last section.

Basically, the Discussion contains several parts, in no particular order, but roughly moving from specific (i.e., related to your experiment only) to general (how your findings fit in the larger scientific community). In this section, you will, as a rule, need to:

Explain whether the data support your hypothesis

• Acknowledge any anomalous data or deviations from what you expected

Derive conclusions, based on your findings, about the process you’re studying

• Relate your findings to earlier work in the same area (if you can)

Explore the theoretical and/or practical implications of your findings

Let’s look at some dos and don’ts for each of these objectives.

This statement is usually a good way to begin the Discussion, since you can’t effectively speak about the larger scientific value of your study until you’ve figured out the particulars of this experiment. You might begin this part of the Discussion by explicitly stating the relationships or correlations your data indicate between the independent and dependent variables. Then you can show more clearly why you believe your hypothesis was or was not supported. For example, if you tested solubility at various temperatures, you could start this section by noting that the rates of solubility increased as the temperature increased. If your initial hypothesis surmised that temperature change would not affect solubility, you would then say something like,

“The hypothesis that temperature change would not affect solubility was not supported by the data.”

Note: Students tend to view labs as practical tests of undeniable scientific truths. As a result, you may want to say that the hypothesis was “proved” or “disproved” or that it was “correct” or “incorrect.” These terms, however, reflect a degree of certainty that you as a scientist aren’t supposed to have. Remember, you’re testing a theory with a procedure that lasts only a few hours and relies on only a few trials, which severely compromises your ability to be sure about the “truth” you see. Words like “supported,” “indicated,” and “suggested” are more acceptable ways to evaluate your hypothesis.

Also, recognize that saying whether the data supported your hypothesis or not involves making a claim to be defended. As such, you need to show the readers that this claim is warranted by the evidence. Make sure that you’re very explicit about the relationship between the evidence and the conclusions you draw from it. This process is difficult for many writers because we don’t often justify conclusions in our regular lives. For example, you might nudge your friend at a party and whisper, “That guy’s drunk,” and once your friend lays eyes on the person in question, she might readily agree. In a scientific paper, by contrast, you would need to defend your claim more thoroughly by pointing to data such as slurred words, unsteady gait, and the lampshade-as-hat. In addition to pointing out these details, you would also need to show how (according to previous studies) these signs are consistent with inebriation, especially if they occur in conjunction with one another. To put it another way, tell your readers exactly how you got from point A (was the hypothesis supported?) to point B (yes/no).

Acknowledge any anomalous data, or deviations from what you expected

You need to take these exceptions and divergences into account, so that you qualify your conclusions sufficiently. For obvious reasons, your readers will doubt your authority if you (deliberately or inadvertently) overlook a key piece of data that doesn’t square with your perspective on what occurred. In a more philosophical sense, once you’ve ignored evidence that contradicts your claims, you’ve departed from the scientific method. The urge to “tidy up” the experiment is often strong, but if you give in to it you’re no longer performing good science.

Sometimes after you’ve performed a study or experiment, you realize that some part of the methods you used to test your hypothesis was flawed. In that case, it’s OK to suggest that if you had the chance to conduct your test again, you might change the design in this or that specific way in order to avoid such and such a problem. The key to making this approach work, though, is to be very precise about the weakness in your experiment, why and how you think that weakness might have affected your data, and how you would alter your protocol to eliminate—or limit the effects of—that weakness. Often, inexperienced researchers and writers feel the need to account for “wrong” data (remember, there’s no such animal), and so they speculate wildly about what might have screwed things up. These speculations include such factors as the unusually hot temperature in the room, or the possibility that their lab partners read the meters wrong, or the potentially defective equipment. These explanations are what scientists call “cop-outs,” or “lame”; don’t indicate that the experiment had a weakness unless you’re fairly certain that a) it really occurred and b) you can explain reasonably well how that weakness affected your results.

If, for example, your hypothesis dealt with the changes in solubility at different temperatures, then try to figure out what you can rationally say about the process of solubility more generally. If you’re doing an undergraduate lab, chances are that the lab will connect in some way to the material you’ve been covering either in lecture or in your reading, so you might choose to return to these resources as a way to help you think clearly about the process as a whole.

This part of the Discussion section is another place where you need to make sure that you’re not overreaching. Again, nothing you’ve found in one study would remotely allow you to claim that you now “know” something, or that something isn’t “true,” or that your experiment “confirmed” some principle or other. Hesitate before you go out on a limb—it’s dangerous! Use less absolutely conclusive language, including such words as “suggest,” “indicate,” “correspond,” “possibly,” “challenge,” etc.

Relate your findings to previous work in the field (if possible)

We’ve been talking about how to show that you belong in a particular community (such as biologists or anthropologists) by writing within conventions that they recognize and accept. Another is to try to identify a conversation going on among members of that community, and use your work to contribute to that conversation. In a larger philosophical sense, scientists can’t fully understand the value of their research unless they have some sense of the context that provoked and nourished it. That is, you have to recognize what’s new about your project (potentially, anyway) and how it benefits the wider body of scientific knowledge. On a more pragmatic level, especially for undergraduates, connecting your lab work to previous research will demonstrate to the TA that you see the big picture. You have an opportunity, in the Discussion section, to distinguish yourself from the students in your class who aren’t thinking beyond the barest facts of the study. Capitalize on this opportunity by putting your own work in context.

If you’re just beginning to work in the natural sciences (as a first-year biology or chemistry student, say), most likely the work you’ll be doing has already been performed and re-performed to a satisfactory degree. Hence, you could probably point to a similar experiment or study and compare/contrast your results and conclusions. More advanced work may deal with an issue that is somewhat less “resolved,” and so previous research may take the form of an ongoing debate, and you can use your own work to weigh in on that debate. If, for example, researchers are hotly disputing the value of herbal remedies for the common cold, and the results of your study suggest that Echinacea diminishes the symptoms but not the actual presence of the cold, then you might want to take some time in the Discussion section to recapitulate the specifics of the dispute as it relates to Echinacea as an herbal remedy. (Consider that you have probably already written in the Introduction about this debate as background research.)

This information is often the best way to end your Discussion (and, for all intents and purposes, the report). In argumentative writing generally, you want to use your closing words to convey the main point of your writing. This main point can be primarily theoretical (“Now that you understand this information, you’re in a better position to understand this larger issue”) or primarily practical (“You can use this information to take such and such an action”). In either case, the concluding statements help the reader to comprehend the significance of your project and your decision to write about it.

Since a lab report is argumentative—after all, you’re investigating a claim, and judging the legitimacy of that claim by generating and collecting evidence—it’s often a good idea to end your report with the same technique for establishing your main point. If you want to go the theoretical route, you might talk about the consequences your study has for the field or phenomenon you’re investigating. To return to the examples regarding solubility, you could end by reflecting on what your work on solubility as a function of temperature tells us (potentially) about solubility in general. (Some folks consider this type of exploration “pure” as opposed to “applied” science, although these labels can be problematic.) If you want to go the practical route, you could end by speculating about the medical, institutional, or commercial implications of your findings—in other words, answer the question, “What can this study help people to do?” In either case, you’re going to make your readers’ experience more satisfying, by helping them see why they spent their time learning what you had to teach them.

Works consulted

We consulted these works while writing this handout. This is not a comprehensive list of resources on the handout’s topic, and we encourage you to do your own research to find additional publications. Please do not use this list as a model for the format of your own reference list, as it may not match the citation style you are using. For guidance on formatting citations, please see the UNC Libraries citation tutorial . We revise these tips periodically and welcome feedback.

American Psychological Association. 2010. Publication Manual of the American Psychological Association . 6th ed. Washington, DC: American Psychological Association.

Beall, Herbert, and John Trimbur. 2001. A Short Guide to Writing About Chemistry , 2nd ed. New York: Longman.

Blum, Deborah, and Mary Knudson. 1997. A Field Guide for Science Writers: The Official Guide of the National Association of Science Writers . New York: Oxford University Press.

Booth, Wayne C., Gregory G. Colomb, Joseph M. Williams, Joseph Bizup, and William T. FitzGerald. 2016. The Craft of Research , 4th ed. Chicago: University of Chicago Press.

Briscoe, Mary Helen. 1996. Preparing Scientific Illustrations: A Guide to Better Posters, Presentations, and Publications , 2nd ed. New York: Springer-Verlag.

Council of Science Editors. 2014. Scientific Style and Format: The CSE Manual for Authors, Editors, and Publishers , 8th ed. Chicago & London: University of Chicago Press.

Davis, Martha. 2012. Scientific Papers and Presentations , 3rd ed. London: Academic Press.

Day, Robert A. 1994. How to Write and Publish a Scientific Paper , 4th ed. Phoenix: Oryx Press.

Porush, David. 1995. A Short Guide to Writing About Science . New York: Longman.

Williams, Joseph, and Joseph Bizup. 2017. Style: Lessons in Clarity and Grace , 12th ed. Boston: Pearson.

You may reproduce it for non-commercial use if you use the entire handout and attribute the source: The Writing Center, University of North Carolina at Chapel Hill

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Medical Student Research Journal

Michigan state university college of human medicine.

Guidelines – Brief Report

Brief reports are similar to original research in that they follow the same rigor, format and guidelines, but are designed for small-scale research or research that is in early stages of development. These may include preliminary studies that utilize a simple research design or a small sample size and that have produced limited pilot data and initial findings that indicate need for further investigation.  Brief reports are much shorter than manuscripts associated with a more advanced, larger-scale research project.  They are not meant to be used for a short version of an article about research that would otherwise qualify for a full original research manuscript or for publishing material on research that lacks significance, is not rigorous or, if expanded, would not qualify for a full article or for research.

Order of Sections

Introduction

• Illustrations (Figure/Tables)

Manuscript Limits

• Total Length: up to 1500 words (not including abstract, illustrations, and references)
• Abstract Length: up to 150 words
• Table/Figure Limits: up to 3 tables and figures
• References: up to 40 references

The title page should carry the following information:

• Article Title (concise information about the study, limited to 100 characters)
• Author Names (Mark D. Smith B.S. 1* , Jeremy Michael Howes M.S. 2 , Jessica P. Masters M.D. Ph. D. 1,2 )
• Institutional Affiliations of all authors in order of appearance in author names ( 1 College of Human Medicine, Michigan State University, East Lansing, MI, USA. 2 Dept. of Internal Medicine, Michigan State University, East Lansing, MI, USA)
• Contact information for corresponding author(s) including address, email, phone, and fax. The corresponding author should indicate clearly whether his or her e-mail address can be published. Corresponding author(s) is indicated with asterisk by name in author listing; the medical students should be corresponding author.
• Short Title (50 character limit shorter title for headers)
• Key Phrases – Relevant words or short phrases that aid search engines or indexes in finding your work (use MeSH headings if possible). Use key phrases that are not present in the title. Maximum of 6, e.g. (Pemphigus, Bullous Skin Disorder, Autoimmune, Blistering, Skin Diseases, Monoclonal Antibody)
• Disclaimers, if any
• Word Count: Abstract and Body (excluding abstract, illustrations, and references)
• Table and Figure Count
• Source(s) of support in the form of grants, equipment, drugs, or all of these
• Conflict of Interest Statement

Abstracts are the only substantive portion of the article indexed in many electronic databases, and the only portion many readers read. Authors must be careful that they accurately reflect the content of the article in the abstract. This description should include:

• Context or background for the study
• Purpose, basic procedures, main findings, and principal conclusions
• Emphasis on new and important aspects of the study or observations
• No references, illustrations, or references to illustrations are allowed in the abstract
• The abstract should be structured with similar headings as in the main article (e.g. introduction, patient profile, interventions, conclusions)

Provide a context or background for the study including the nature of the problem and its significance to medical learners and scientists. State the specific purpose or research objective tested by the study; the research objective is often more sharply focused when stated as a question. Both the main and secondary objectives should be clear, and any pre-specified subgroup analyses should be described. Provide only directly pertinent references, and do not include data or conclusions from the work being reported.

The methods section should include information on materials, methods and procedures in sufficient detail such that the study can be repeated and/or validated. The methods section should include only information that was available at the time the plan or protocol for the study was being written; all information obtained during the study belongs in the results section. The methods section is comprised of the following subheadings (if applicable):

Selection and Description of Participants

Describe your selection of the observational or experimental participants clearly, including eligibility and exclusion criteria and a description of the source population. Explain the use of variables such as gender and age when they are included in a study report–for example, authors should explain why only participants of certain ages were included or why women were excluded. The guiding principle should be clarity about how and why a study was done in a particular way.

Technical Information

Identify the methods, apparatus (give the manufacturer’s name, city, and state in parentheses), and procedures in sufficient detail to allow others to reproduce the results. Give references to established methods, including statistical methods, describe new or substantially modified methods, give the reasons for using them, and evaluate their limitations.

Describe statistical methods with enough detail to enable a knowledgeable reader with access to the original data to verify the reported results. Avoid relying solely on statistical hypothesis testing, such as p-values, which fail to convey important information about effect size, but include averages with confidence intervals if available, in addition to the p-values. Define statistical terms, abbreviations, and most symbols. Specify the computer software used.

Present your results in logical sequence in the text, tables, and illustrations, giving the main or most important findings first. Do not repeat all the data in the tables or illustrations in the text; emphasize or summarize only the most important observations. Extra or supplementary materials and technical detail can be placed in an appendix. Restrict tables and figures to those needed to explain the argument of the paper and to assess supporting data. Avoid interpreting the data, as this section is pure information that the reader can interpret for themselves; the authors’ own interpretation of the data is meant for the discussion section of the manuscript.

Emphasize the new and important aspects of the study and the conclusions that follow from them. For experimental studies, it is useful to begin the discussion by summarizing briefly the main findings, then explore possible mechanisms or explanations for these findings, compare and contrast the results with other relevant studies, state the limitations of the study, and explore the implications of the findings for future research and for clinical practice.

Limitations : Because every experiment or project can always be improved, a healthy discussion of the limitations of the study should be included. All manuscripts should include a brief discussion on the adequacy of the research methods to draw a valid conclusion. Authors should comment on changes that would improve the methods of the study or reasons why the methods are able to draw a strong conclusion. The discussion of limitations should not be a separate heading or sub-heading in the actual manuscript, but should be a flowing part of the discussion section.

Please see the directions for formatting References .

Illustrations (Figures and Tables)

Should follow the same format as mentioned in the General Guidelines .

Copyright Notice

The Medical Student Research Journal is a scholarly journal by medical students supported by Michigan State University College of Human Medicine.

Authors grant unrestricted copyright license to MSRJ while retaining full copyright to their works. This means authors may distribute their work at their discretion, but may not alter the final product as published by MSRJ. Permissions for reproduction of final published article are granted by MSRJ, while pre-publication works are distributable at authors’ discretion.

Michigan State University logos used with permission.

Privacy Statement

The names and email addresses entered in this journal site will be used exclusively for the stated purposes of this journal and will not be made available for any other purpose or to any other party.

Disclosures Please view our disclosures available on the general guidelines here .

This page was last updated on 2/15/2014 KCP.

Best Medical Summary Report

Report generator.

A medical report template is a document used to provide a summary report of your medical history. You may be required to provide this document when applying for a new job, buying a new car, seeking medical assistance from a medical facility in a different city, and so on. Below are examples of sample templates that you can download and use to present a summary of your medical report when the need for it arises.

1. Hospital Weekly Doctor Medical Report

• Google Docs

2. Medical Patient Summary Report Template

3. Clinical Trail Summary Results Report

4. Customer Medical Report Example Template

Defining a Medical Summary Report:

A Medical Summary Report (MSR) is a comprehensive document that encapsulates a patient’s medical history, current conditions, treatment plans, and progress notes. It provides a concise yet detailed overview of the patient’s interactions with healthcare systems and professionals. It’s often used to communicate between different healthcare providers, for insurance purposes, or for legal documentation.

Components of a Medical Summary Report:

A Medical Summary Report (MSR) is a comprehensive document that provides an overview of a patient’s medical history and current clinical status. Here are the typical components of an MSR:

Patient Demographics:

• Name : Patient’s full legal name.
• Date of Birth : To identify the age which can be relevant to the medical condition.
• Gender : May influence disease risk and treatment response.
• Identification Number : Such as Social Security or patient ID.

Medical History:

• Past Medical History : Summary of past diseases, surgeries, hospitalizations, and outcomes.
• Family History : Any significant diseases that run in the family.
• Social History : Lifestyle factors such as smoking, alcohol use, occupation, and living conditions.
• Allergies : Listing of all known allergies, especially drug allergies.

Current Clinical Status:

• Chief Complaint : The patient’s primary concern or symptom.
• History of Present Illness : Detailed description of the current complaint, including the onset, duration, and any associated symptoms.
• Medications : Current prescriptions, over-the-counter drugs, and supplements.

Diagnostic Findings:

• Laboratory Tests : Results of blood tests, urine tests, etc.
• Imaging Studies : X-rays, MRIs, CT scans, etc., and their results.
• Specialist Consultations : Opinions from specialist doctors if applicable.

Assessment:

• Diagnoses : List of current diagnoses based on the medical history and diagnostic findings.
• Problem List : A prioritized list of medical issues that need management.

Plan of Care:

• Treatment Plan : Detailed description of the therapeutic approach, including medications, surgeries, therapies, and lifestyle changes.
• Rehabilitation Plan : If applicable, outline the rehabilitation goals and methods.
• Prognosis : Expected outcome of the condition and treatment.

Progress Notes:

• Clinical Progress : Documentation of the patient’s response to treatment over time.
• Updates to Plan of Care : Changes made to the treatment plan based on the progress.

Summary and Recommendations:

• Executive Summary : Brief overview of the patient’s condition, highlighting the most critical aspects of the medical report.
• Recommendations : Next steps in terms of treatment, further testing, or referrals to specialists.

It’s that time in your life when you are ready to own a new car. You are so excited that you can’t wait to go for a test drive and eventually bring your dream motor home with you. However, before a motor dealer accepts your payment and hands you a license and keys, they’ll need to see your medical report. At the end of the day, the motor seller needs to be sure that you are physically fit and healthy to own and drive a car. You can use this medical report template to request a summary of your health status from your physician.

5. Medical Condition Report for Operation Format

Size: 661 kB

The Integral Role of a Medical Summary Report:

Facilitating continuity of care:.

The primary purpose of a medical summary report is to ensure continuity of care. It provides a comprehensive overview of a patient’s medical history, current condition, and treatment plan, which is essential for seamless care coordination among various healthcare providers.

Informing Clinical Decisions:

Medical summary reports serve as a critical reference point for clinicians, offering the necessary insights to inform and guide clinical decisions. This includes diagnosis, treatment options, and management of the patient’s condition.

Enhancing Patient Communication:

These reports are vital for communicating complex medical information in a structured and understandable format, aiding in discussions with patients and their families about their health status and treatment strategies.

Supporting Administrative Functions:

Medical summary reports are indispensable for administrative purposes, such as billing, coding, and insurance claims. They provide a documented account of the services provided and the medical necessity of those services.

Legal and Compliance Documentation:

In legal scenarios, such as personal injury cases or medical malpractice claims, medical summary reports provide a clear, concise record of medical events and are often used as evidence. They also ensure compliance with medical documentation standards and legal requirements.

Streamlining Review Processes:

For healthcare professionals and institutions conducting audits, quality control, or peer reviews, medical summary reports offer a streamlined way to assess the quality of care and adherence to treatment protocols.

Enhancing Patient Safety:

By documenting a patient’s allergies, past reactions to treatments, and other critical health information, medical summary reports play a crucial role in enhancing patient safety and preventing medical errors.

Facilitating Research and Education:

Medical summary reports can be used for research purposes, helping to identify trends, outcomes, and areas for improvement in patient care . They also serve as educational tools for medical students and trainees.

Buying and owning a car is an achievement. And, if you have the finances, nothing can stop you from purchasing your favorite model. However, your motor dealer may not sell you a car right off the bat. They may request to see a  sample report of your medical condition before selling to you. It is important to provide this document when requested. It helps the dealer to determine if really you are medically fit to drive a car. In the end, this report will help the dealership to determine whether you are fit to drive or still need medical attention.

6. Physical Examination Record Report Template

Size: 827 kB

What Constitutes an Effective Medical Summary Report?

Clarity and conciseness:.

An effective medical summary report should be clear and concise, allowing readers to quickly grasp the essential information. Medical jargon should be used judiciously, ensuring that the report is accessible to non-specialists if necessary.

Comprehensive Patient Information:

The report must include comprehensive patient information, such as the patient’s full name, date of birth, medical record number, and the date of the report. This ensures that the report can be accurately matched to the correct patient.

Detailed Medical History:

A thorough medical history is crucial. This includes past medical issues, surgeries, allergies, medications, and family medical history. This information provides context for the patient’s current medical condition.

The current clinical status, including recent diagnoses, treatment plans, and medications, should be detailed. This section should also cover any recent tests, their results, and their implications for the patient’s care.

Treatment Plan and Recommendations:

A clear treatment plan and recommendations are essential. This should outline the proposed medical interventions, follow-up appointments, and any lifestyle changes or home care instructions.

Accurate and Relevant Test Results:

Test results should be presented accurately and relevantly. Only include tests that are pertinent to the current medical issues to avoid overwhelming the reader with unnecessary data.

The prognosis, including expected outcomes and potential complications, should be clearly stated. This helps in setting realistic expectations for the patient and their caregivers.

Signature and Credentials:

The report must be signed by the healthcare provider with their credentials and contact information. This adds a layer of accountability and provides a point of contact for follow-up questions.

Patient-Centered Approach:

The report should always keep the patient’s needs and concerns at the forefront. This includes respecting their privacy and ensuring that the report is understandable to those who may be involved in their care.

A medical summary report, as we know it, is a very important note that you should be willing to provide upon request. Anyone engaging you in a professional dealership needs to know that you are healthy and physically fit. The department of transportation takes health and safety concerns seriously. More often than not, they’ll need to see your medical report to determine whether you are fit enough to operate a commercial motor vehicle. Therefore, it is important to talk to your doctor about this and have them provide the necessary information. You can download and use this PDF file to present the report to the relevant department. You may also see medical release form .

7. Child Health Medical Report Form Sample

Size: 11 kB

Do you know that you can download a custom medical examination report  and never have to design one from scratch? With that said, here is a simple, one-page PDF file that you can download and use straight away. Both you and your doctor (examiner) will have to fill out this document.

8. Printable Patient Medical Report Template

Size: 178 kB

Essential Elements of Medical Record Reviews:

Purpose-driven analysis:.

Medical record reviews must be conducted with a clear purpose, whether for quality assurance, legal examination, or clinical activity report summary. The objective guides the depth and focus of the review.

Comprehensive Data Collection:

A meticulous collection of data from the patient’s entire medical history is paramount. This includes hospitalizations, physician notes, diagnostic tests, and treatment outcomes.

Chronological Organization:

Organizing the information chronologically provides a timeline of medical events, aiding in understanding the progression of the patient’s condition and the interventions made.

Attention to Detail:

Attention to detail is critical. Accurate interpretation of medical records can impact patient care decisions, legal outcomes, or insurance claims.

Critical Analysis:

A critical analysis of the medical records is necessary to identify patterns, inconsistencies, or gaps in the medical care provided.

Summary of Findings:

A clear summary of findings should be presented at the end of the review, highlighting the key points and relevant details that support the purpose of the review.

Confidentiality and Compliance:

Maintaining patient confidentiality is non-negotiable, and the review process must comply with HIPAA and other relevant regulations.

Expertise and Objectivity:

The reviewer should have the necessary medical expertise and remain objective, avoiding any bias that could skew the review.

Clear Reporting:

The findings should be reported in a clear, structured manner, using language that is accessible to non-medical professionals if the review is for a broader audience.

Use of Technology:

Leveraging technology for electronic health records can streamline the review process, making it easier to search for and analyze specific data points.

9. Simple Medical History Records Report Template

Size: 45 KB

We know that creating a report template isn’t easy. If anything, it takes a lot of time to put the design and the content together. The best way to save time on design has always been to use a pre-designed template. It’s the same medical case summary with a medical report template. Instead of spending hours behind your computer trying your best to create a template that stands out, you can download this PDF file and use it straight away. Remember, the content of the document should remain confidential between you and the person to whom you submit the file.

10. Sample Student Medical Report Example

Size: 206 KB

How to write a Medical Report?

Writing a medical report involves a structured approach to ensure all relevant clinical information is accurately and clearly presented. Here’s a general guide on how to write a medical report:

• Patient’s Name : Confidentiality should be maintained if the report is for third-party use.
• Date of Birth : To help accurately identify the patient.
• Date of Examination : To provide a timeline of the medical evaluation summary .
• Medical Record Number : If applicable, for hospital or clinic records.
• Date of Report : The date the report is written.

Introduction:

• Purpose of the Report : Explain why the report is being written (e.g., for insurance purposes, legal reasons, etc.).
• Requesting Party : Identify who requested the report, if relevant.

Patient Information:

• Chief Complaint : The main reason for the patient’s visit or concern.
• History of Present Illness : A detailed account of the patient’s current complaints, including onset, duration, intensity, and character of symptoms.
• Past Medical History : Any relevant past illnesses, surgeries, or ongoing health issues.
• Medications : List current medications including dosages.
• Allergies : Any known allergies, especially to medications.

Clinical Findings:

• Physical Examination : Document vital signs and findings from the physical exam, organized by system (cardiovascular, respiratory, etc.).
• Diagnostic Test Results : Summarize results from lab tests, imaging studies, etc.

Assessment and Plan:

• Diagnoses : List the diagnoses derived from the clinical findings.
• Treatment Plan : Outline the recommended treatment plan, including medications, therapies, or surgical interventions.
• Prognosis : Provide an opinion on the likely course and outcome of the condition.
• Summarize the key points from the report.
• Offer recommendations for future management or assessment.
• Indicate if follow-up is needed, and if so, when and why.
• Doctor’s Name and Signature : Ensure the report is signed by the healthcare provider who prepared it.
• Qualifications : Include the credentials of the healthcare provider.
• Contact Information : Provide a way to contact the healthcare provider for clarifications.

Do you know that you must provide a student medical report before joining college or university? The health report is important because the school administration wants to know about the current state of your health before admitting you. According to this sample document, a school may want to know whether you are immune to certain ailments or not. So, it is important to make sure you provide as much information as possible to make your condition understood. Download the sample PDF file and use it to prepare the medical report. This is an all-rounded template, which means it is acceptable in any school. You may also see investigation summary report .

11. Medical Summary Report for Job Hunting

Size: 345 kB

One of the most important things that a potential employer may ask for before hiring is your medical report sample file . But, even if they do not request for it, it is still important to make sure you present the document so they know the kind of person they’ll be working with months and probably years to come. The easiest way to get the medical report ready is to use a pre-built template. This one here is an example of a professionally designed template that you can download, print, make a copy if need be, and then use to prepare the medical report.

12. Medical Treatment Report Summary Template

Size: 83.1 KB

Crafting a Medical Summary Report: A Step-by-Step Guide

When writing an MSR, it’s important to be clear, accurate, and concise, ensuring that all relevant information is easy to find and understand. It should be tailored to the audience—it needs to be comprehensive enough for a healthcare professional but also accessible for patients and non-medical personnel if necessary. The language should be professional and avoid jargon when not writing exclusively for a medical audience. The MSR should be up-to-date, reflecting the latest entries in the patient’s medical records.

Step 1: Gather Comprehensive Patient Data

Begin by collecting all pertinent patient information, including personal identification, medical history, and any previous and current treatments or medications.

Step 2: Review and Organize Medical Records

Review the patient’s medical records in detail. Organize the information chronologically to provide a clear timeline of medical events and treatments.

Step 3: Summarize Medical History

Create a section dedicated to the patient’s medical history. Include past diagnoses, treatments, surgical procedures, allergies, and any chronic conditions.

Step 4: Detail Current Medical Status

Outline the patient’s current medical status. Describe recent diagnoses, the results of diagnostic tests, and any ongoing treatments or medications.

Step 5: Describe Treatment Plan

Clearly articulate the treatment plan, including medications, therapies, surgeries, or other interventions. Specify dosages, frequency, and duration of treatments where applicable.

Step 6: Include Test Results and Interpretations

Present recent test results, along with interpretations from healthcare providers. Ensure that this information is relevant to the current medical assessment and plan.

Step 7: Provide Prognosis

Offer a prognosis based on the patient’s current condition, treatment responses, and any other relevant factors. This should be realistic and based on available data.

Step 8: Format the Report for Clarity

Use headings, subheadings, bullet points, and tables to make the report easy to navigate. Ensure that each section is clearly labeled and that the information flows logically.

Step 9: Review for Accuracy and Completeness

Carefully review the report to ensure that all information is accurate, complete, and presented in a professional manner. Verify that no essential information is missing.

Step 10: Finalize the Report

Conclude the report with any final recommendations or follow-up actions. Include the name and contact information of the healthcare provider preparing the report, and ensure it is properly signed and dated.

There may come a time when you have to provide a  medical case report about your health. In such a case, you may need to write and present your medical summary report to the respective party. Unfortunately, writing this document isn’t often as easy as many people think. While you can write it on your own, you will need the help of your physician to provide accurate information. This PDF example shows what a sample medical summary report looks like. It is a good template to download and read to understand what makes an outstanding report on health and wellness. You may also like to see case study summary

13. Medical Summary Report for School

Size: 272 KB

Medical Report vs Discharge Summary

Medical reports and discharge summaries are both crucial documents in the healthcare process, serving different yet interconnected purposes. Here’s a table differentiating the two:

Both the medical report and discharge summary are essential for ensuring quality continuity of care, with the medical report providing an ongoing record and the discharge summary acting as a transitional document as the patient leaves the hospital setting.

Who prepares a Medical Summary Report ?

A medical summary report is typically prepared by healthcare professionals, such as physicians, nurses, or medical record technicians, who have access to a patient’s medical history and current health information. These professionals ensure the report is accurate, comprehensive, and reflective of the patient’s medical journey.

What role does a Medical Summary Report play in patient care?

A Medical Summary Report plays a crucial role in patient care by providing a comprehensive overview of a patient’s medical history, current condition, and treatment plan, facilitating continuity of care, informing clinical decisions, and ensuring all healthcare providers are aligned in their understanding and approach to a patient’s health management.

How often should a Medical Summary Report be updated?

A Medical Summary Report should be updated whenever there is a significant change in the patient’s condition, treatment, or when new diagnostic information becomes available. Regular updates after major appointments, procedures, or at set intervals, such as annually, ensure the report remains current and useful for ongoing patient care.

Can patients access their own Medical Discharge Summary Report?

Yes, patients have the right to access their own Medical Discharge Summary Report. This document is provided upon discharge summary from a hospital or healthcare facility, and patients can request a copy for their records. It’s an important part of patient rights and healthcare transparency.

What privacy laws protect Medical Summary Reports?

The Health Insurance Portability and Accountability Act (HIPAA) in the United States protects the privacy of Medical Summary Reports. It sets standards for the security of electronic protected health information and dictates how personally identifiable information maintained by the healthcare and healthcare insurance industries should be protected from fraud and theft.

To ensure that a church remains a peaceful place of worship, ministry management always do the best possible to make sure leaders are fit to perform their duties. Take the music department, for example. It is the responsibility of the team leader to ensure that the church band has the skills required to lead people in praise and worship. Another important thing to check is the medical report of individuals who want to get into the ministry. Since the management in your church may ask for your health sample report before ordaining you to serve, consider using this PDF template to provide the information.

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How to write statistical analysis section in medical research

Alok kumar dwivedi.

Department of Molecular and Translational Medicine, Division of Biostatistics and Epidemiology, Texas Tech University Health Sciences Center El Paso, El Paso, Texas, USA

Associated Data

jim-2022-002479supp001.pdf

Data sharing not applicable as no datasets generated and/or analyzed for this study.

Reporting of statistical analysis is essential in any clinical and translational research study. However, medical research studies sometimes report statistical analysis that is either inappropriate or insufficient to attest to the accuracy and validity of findings and conclusions. Published works involving inaccurate statistical analyses and insufficient reporting influence the conduct of future scientific studies, including meta-analyses and medical decisions. Although the biostatistical practice has been improved over the years due to the involvement of statistical reviewers and collaborators in research studies, there remain areas of improvement for transparent reporting of the statistical analysis section in a study. Evidence-based biostatistics practice throughout the research is useful for generating reliable data and translating meaningful data to meaningful interpretation and decisions in medical research. Most existing research reporting guidelines do not provide guidance for reporting methods in the statistical analysis section that helps in evaluating the quality of findings and data interpretation. In this report, we highlight the global and critical steps to be reported in the statistical analysis of grants and research articles. We provide clarity and the importance of understanding study objective types, data generation process, effect size use, evidence-based biostatistical methods use, and development of statistical models through several thematic frameworks. We also provide published examples of adherence or non-adherence to methodological standards related to each step in the statistical analysis and their implications. We believe the suggestions provided in this report can have far-reaching implications for education and strengthening the quality of statistical reporting and biostatistical practice in medical research.

Introduction

Biostatistics is the overall approach to how we realistically and feasibly execute a research idea to produce meaningful data and translate data to meaningful interpretation and decisions. In this era of evidence-based medicine and practice, basic biostatistical knowledge becomes essential for critically appraising research articles and implementing findings for better patient management, improving healthcare, and research planning. 1 However, it may not be sufficient for the proper execution and reporting of statistical analyses in studies. 2 3 Three things are required for statistical analyses, namely knowledge of the conceptual framework of variables, research design, and evidence-based applications of statistical analysis with statistical software. 4 5 The conceptual framework provides possible biological and clinical pathways between independent variables and outcomes with role specification of variables. The research design provides a protocol of study design and data generation process (DGP), whereas the evidence-based statistical analysis approach provides guidance for selecting and implementing approaches after evaluating data with the research design. 2 5 Ocaña-Riola 6 reported a substantial percentage of articles from high-impact medical journals contained errors in statistical analysis or data interpretation. These errors in statistical analyses and interpretation of results do not only impact the reliability of research findings but also influence the medical decision-making and planning and execution of other related studies. A survey of consulting biostatisticians in the USA reported that researchers frequently request biostatisticians for performing inappropriate statistical analyses and inappropriate reporting of data. 7 This implies that there is a need to enforce standardized reporting of the statistical analysis section in medical research which can also help rreviewers and investigators to improve the methodological standards of the study.

Biostatistical practice in medicine has been improving over the years due to continuous efforts in promoting awareness and involving expert services on biostatistics, epidemiology, and research design in clinical and translational research. 8–11 Despite these efforts, the quality of reporting of statistical analysis in research studies has often been suboptimal. 12 13 We noticed that none of the methods reporting documents were developed using evidence-based biostatistics (EBB) theory and practice. The EBB practice implies that the selection of statistical analysis methods for statistical analyses and the steps of results reporting and interpretation should be grounded based on the evidence generated in the scientific literature and according to the study objective type and design. 5 Previous works have not properly elucidated the importance of understanding EBB concepts and related reporting in the write-up of statistical analyses. As a result, reviewers sometimes ask to present data or execute analyses that do not match the study objective type. 14 We summarize the statistical analysis steps to be reported in the statistical analysis section based on review and thematic frameworks.

We identified articles describing statistical reporting problems in medicine using different search terms ( online supplemental table 1 ). Based on these studies, we prioritized commonly reported statistical errors in analytical strategies and developed essential components to be reported in the statistical analysis section of research grants and studies. We also clarified the purpose and the overall implication of reporting each step in statistical analyses through various examples.

Supplementary data

Although biostatistical inputs are critical for the entire research study ( online supplemental table 2 ), biostatistical consultations were mostly used for statistical analyses only 15 . Even though the conduct of statistical analysis mismatched with the study objective and DGP was identified as the major problem in articles submitted to high-impact medical journals. 16 In addition, multivariable analyses were often inappropriately conducted and reported in published studies. 17 18 In light of these statistical errors, we describe the reporting of the following components in the statistical analysis section of the study.

Step 1: specify study objective type and outcomes (overall approach)

The study objective type provides the role of important variables for a specified outcome in statistical analyses and the overall approach of the model building and model reporting steps in a study. In the statistical framework, the problems are classified into descriptive and inferential/analytical/confirmatory objectives. In the epidemiological framework, the analytical and prognostic problems are broadly classified into association, explanatory, and predictive objectives. 19 These study objectives ( figure 1 ) may be classified into six categories: (1) exploratory, (2) association, (3) causal, (4) intervention, (5) prediction and (6) clinical decision models in medical research. 20

Comparative assessments of developing and reporting of study objective types and models. Association measures include odds ratio, risk ratio, or hazard ratio. AUC, area under the curve; C, confounder; CI, confidence interval; E, exposure; HbA1C: hemoglobin A1c; M, mediator; MFT, model fit test; MST, model specification test; PI, predictive interval; R 2 , coefficient of determinant; X, independent variable; Y, outcome.

The exploratory objective type is a specific type of determinant study and is commonly known as risk factors or correlates study in medical research. In an exploratory study, all covariates are considered equally important for the outcome of interest in the study. The goal of the exploratory study is to present the results of a model which gives higher accuracy after satisfying all model-related assumptions. In the association study, the investigator identifies predefined exposures of interest for the outcome, and variables other than exposures are also important for the interpretation and considered as covariates. The goal of an association study is to present the adjusted association of exposure with outcome. 20 In the causal objective study, the investigator is interested in determining the impact of exposure(s) on outcome using the conceptual framework. In this study objective, all variables should have a predefined role (exposures, confounders, mediators, covariates, and predictors) in a conceptual framework. A study with a causal objective is known as an explanatory or a confirmatory study in medical research. The goal is to present the direct or indirect effects of exposure(s) on an outcome after assessing the model’s fitness in the conceptual framework. 19 21 The objective of an interventional study is to determine the effect of an intervention on outcomes and is often known as randomized or non-randomized clinical trials in medical research. In the intervention objective model, all variables other than the intervention are treated as nuisance variables for primary analyses. The goal is to present the direct effect of the intervention on the outcomes by eliminating biases. 22–24 In the predictive study, the goal is to determine an optimum set of variables that can predict the outcome, particularly in external settings. The clinical decision models are a special case of prognostic models in which high dimensional data at various levels are used for risk stratification, classification, and prediction. In this model, all variables are considered input features. The goal is to present a decision tool that has high accuracy in training, testing, and validation data sets. 20 25 Biostatisticians or applied researchers should properly discuss the intention of the study objective type before proceeding with statistical analyses. In addition, it would be a good idea to prepare a conceptual model framework regardless of study objective type to understand study concepts.

A study 26 showed a favorable effect of the beta-blocker intervention on survival outcome in patients with advanced human epidermal growth factor receptor (HER2)-negative breast cancer without adjusting for all the potential confounding effects (age or menopausal status and Eastern Cooperative Oncology Performance Status) in primary analyses or validation analyses or using a propensity score-adjusted analysis, which is an EBB preferred method for analyzing non-randomized studies. 27 Similarly, another study had the goal of developing a predictive model for prediction of Alzheimer’s disease progression. 28 However, this study did not internally or externally validate the performance of the model as per the requirement of a predictive objective study. In another study, 29 investigators were interested in determining an association between metabolic syndrome and hepatitis C virus. However, the authors did not clearly specify the outcome in the analysis and produced conflicting associations with different analyses. 30 Thus, the outcome should be clearly specified as per the study objective type.

Step 2: specify effect size measure according to study design (interpretation and practical value)

The study design provides information on the selection of study participants and the process of data collection conditioned on either exposure or outcome ( figure 2 ). The appropriate use of effect size measure, tabular presentation of results, and the level of evidence are mostly determined by the study design. 31 32 In cohort or clinical trial study designs, the participants are selected based on exposure status and are followed up for the development of the outcome. These study designs can provide multiple outcomes, produce incidence or incidence density, and are preferred to be analyzed with risk ratio (RR) or hazards models. In a case–control study, the selection of participants is conditioned on outcome status. This type of study can have only one outcome and is preferred to be analyzed with an odds ratio (OR) model. In a cross-sectional study design, there is no selection restriction on outcomes or exposures. All data are collected simultaneously and can be analyzed with a prevalence ratio model, which is mathematically equivalent to the RR model. 33 The reporting of effect size measure also depends on the study objective type. For example, predictive models typically require reporting of regression coefficients or weight of variables in the model instead of association measures, which are required in other objective types. There are agreements and disagreements between OR and RR measures. Due to the constancy and symmetricity properties of OR, some researchers prefer to use OR in studies with common events. Similarly, the collapsibility and interpretability properties of RR make it more appealing to use in studies with common events. 34 To avoid variable practice and interpretation issues with OR, it is recommended to use RR models in all studies except for case–control and nested case–control studies, where OR approximates RR and thus OR models should be used. Otherwise, investigators may report sufficient data to compute any ratio measure. Biostatisticians should educate investigators on the proper interpretation of ratio measures in the light of study design and their reporting. 34 35

Effect size according to study design.

Investigators sometimes either inappropriately label their study design 36 37 or report effect size measures not aligned with the study design, 38 39 leading to difficulty in results interpretation and evaluation of the level of evidence. The proper labeling of study design and the appropriate use of effect size measure have substantial implications for results interpretation, including the conduct of systematic review and meta-analysis. 40 A study 31 reviewed the frequency of reporting OR instead of RR in cohort studies and randomized clinical trials (RCTs) and found that one-third of the cohort studies used an OR model, whereas 5% of RCTs used an OR model. The majority of estimated ORs from these studies had a 20% or higher deviation from the corresponding RR.

Step 3: specify study hypothesis, reporting of p values, and interval estimates (interpretation and decision)

The clinical hypothesis provides information for evaluating formal claims specified in the study objectives, while the statistical hypothesis provides information about the population parameters/statistics being used to test the formal claims. The inference about the study hypothesis is typically measured by p value and confidence interval (CI). A smaller p value indicates that the data support against the null hypothesis. Since the p value is a conditional probability, it can never tell about the acceptance or rejection of the null hypothesis. Therefore, multiple alternative strategies of p values have been proposed to strengthen the credibility of conclusions. 41 42 Adaption of these alternative strategies is only needed in the explanatory objective studies. Although exact p values are recommended to be reported in research studies, p values do not provide any information about the effect size. Compared with p values, the CI provides a confidence range of the effect size that contains the true effect size if the study were repeated and can be used to determine whether the results are statistically significant or not. 43 Both p value and 95% CI provide complementary information and thus need to be specified in the statistical analysis section. 24 44

Researchers often test one or more comparisons or hypotheses. Accordingly, the side and the level of significance for considering results to be statistically significant may change. Furthermore, studies may include more than one primary outcome that requires an adjustment in the level of significance for multiplicity. All studies should provide the interval estimate of the effect size/regression coefficient in the primary analyses. Since the interpretation of data analysis depends on the study hypothesis, researchers are required to specify the level of significance along with the side (one-sided or two-sided) of the p value in the test for considering statistically significant results, adjustment of the level of significance due to multiple comparisons or multiplicity, and reporting of interval estimates of the effect size in the statistical analysis section. 45

A study 46 showed a significant effect of fluoxetine on relapse rates in obsessive-compulsive disorder based on a one-sided p value of 0.04. Clearly, there was no reason for using a one-sided p value as opposed to a two-sided p value. A review of the appropriate use of multiple test correction methods in multiarm clinical trials published in major medical journals in 2012 identified over 50% of the articles did not perform multiple-testing correction. 47 Similar to controlling a familywise error rate due to multiple comparisons, adjustment of the false discovery rate is also critical in studies involving multiple related outcomes. A review of RCTs for depression between 2007 and 2008 from six journals reported that only limited studies (5.8%) accounted for multiplicity in the analyses due to multiple outcomes. 48

Step 4: account for DGP in the statistical analysis (accuracy)

The study design also requires the specification of the selection of participants and outcome measurement processes in different design settings. We referred to this specific design feature as DGP. Understanding DGP helps in determining appropriate modeling of outcome distribution in statistical analyses and setting up model premises and units of analysis. 4 DGP ( figure 3 ) involves information on data generation and data measures, including the number of measurements after random selection, complex selection, consecutive selection, pragmatic selection, or systematic selection. Specifically, DGP depends on a sampling setting (participants are selected using survey sampling methods and one subject may represent multiple participants in the population), clustered setting (participants are clustered through a recruitment setting or hierarchical setting or multiple hospitals), pragmatic setting (participants are selected through mixed approaches), or systematic review setting (participants are selected from published studies). DGP also depends on the measurements of outcomes in an unpaired setting (measured on one occasion only in independent groups), paired setting (measured on more than one occasion or participants are matched on certain subject characteristics), or mixed setting (measured on more than one occasion but interested in comparing independent groups). It also involves information regarding outcomes or exposure generation processes using quantitative or categorical variables, quantitative values using labs or validated instruments, and self-reported or administered tests yielding a variety of data distributions, including individual distribution, mixed-type distribution, mixed distributions, and latent distributions. Due to different DGPs, study data may include messy or missing data, incomplete/partial measurements, time-varying measurements, surrogate measures, latent measures, imbalances, unknown confounders, instrument variables, correlated responses, various levels of clustering, qualitative data, or mixed data outcomes, competing events, individual and higher-level variables, etc. The performance of statistical analysis, appropriate estimation of standard errors of estimates and subsequently computation of p values, the generalizability of findings, and the graphical display of data rely on DGP. Accounting for DGP in the analyses requires proper communication between investigators and biostatisticians about each aspect of participant selection and data collection, including measurements, occasions of measurements, and instruments used in the research study.

Common features of the data generation process.

A study 49 compared the intake of fresh fruit and komatsuna juice with the intake of commercial vegetable juice on metabolic parameters in middle-aged men using an RCT. The study was criticized for many reasons, but primarily for incorrect statistical methods not aligned with the study DGP. 50 Similarly, another study 51 highlighted that 80% of published studies using the Korean National Health and Nutrition Examination Survey did not incorporate survey sampling structure in statistical analyses, producing biased estimates and inappropriate findings. Likewise, another study 52 highlighted the need for maintaining methodological standards while analyzing data from the National Inpatient Sample. A systematic review 53 identified that over 50% of studies did not specify whether a paired t-test or an unpaired t-test was performed in statistical analysis in the top 25% of physiology journals, indicating poor transparency in reporting of statistical analysis as per the data type. Another study 54 also highlighted the data displaying errors not aligned with DGP. As per DGP, delay in treatment initiation of patients with cancer defined from the onset of symptom to treatment initiation should be analyzed into three components: patient/primary delay, secondary delay, and tertiary delay. 55 Similarly, the number of cancerous nodes should be analyzed with count data models. 56 However, several studies did not analyze such data according to DGP. 57 58

Step 5: apply EBB methods specific to study design features and DGP (efficiency and robustness)

The continuous growth in the development of robust statistical methods for dealing with a specific problem produced various methods to analyze specific data types. Since multiple methods are available for handling a specific problem yet with varying performances, heterogeneous practices among applied researchers have been noticed. Variable practices could also be due to a lack of consensus on statistical methods in literature, unawareness, and the unavailability of standardized statistical guidelines. 2 5 59 However, it becomes sometimes difficult to differentiate whether a specific method was used due to its robustness, lack of awareness, lack of accessibility of statistical software to apply an alternative appropriate method, intention to produce expected results, or ignorance of model diagnostics. To avoid heterogeneous practices, the selection of statistical methodology and their reporting at each stage of data analysis should be conducted using methods according to EBB practice. 5 Since it is hard for applied researchers to optimally select statistical methodology at each step, we encourage investigators to involve biostatisticians at the very early stage in basic, clinical, population, translational, and database research. We also appeal to biostatisticians to develop guidelines, checklists, and educational tools to promote the concept of EBB. As an effort, we developed the statistical analysis and methods in biomedical research (SAMBR) guidelines for applied researchers to use EBB methods for data analysis. 5 The EBB practice is essential for applying recent cutting-edge robust methodologies to yield accurate and unbiased results. The efficiency of statistical methodologies depends on the assumptions and DGP. Therefore, investigators may attempt to specify the choice of specific models in the primary analysis as per the EBB.

Although details of evidence-based preferred methods are provided in the SAMBR checklists for each study design/objective, 5 we have presented a simplified version of evidence-based preferred methods for common statistical analysis ( online supplemental table 3 ). Several examples are available in the literature where inefficient methods not according to EBB practice have been used. 31 57 60

Step 6: report variable selection method in the multivariable analysis according to study objective type (unbiased)

Multivariable analysis can be used for association, prediction or classification or risk stratification, adjustment, propensity score development, and effect size estimation. 61 Some biological, clinical, behavioral, and environmental factors may directly associate or influence the relationship between exposure and outcome. Therefore, almost all health studies require multivariable analyses for accurate and unbiased interpretations of findings ( figure 1 ). Analysts should develop an adjusted model if the sample size permits. It is a misconception that the analysis of RCT does not require adjusted analysis. Analysis of RCT may require adjustment for prognostic variables. 23 The foremost step in model building is the entry of variables after finalizing the appropriate parametric or non-parametric regression model. In the exploratory model building process due to no preference of exposures, a backward automated approach after including any variables that are significant at 25% in the unadjusted analysis can be used for variable selection. 62 63 In the association model, a manual selection of covariates based on the relevance of the variables should be included in a fully adjusted model. 63 In a causal model, clinically guided methods should be used for variable selection and their adjustments. 20 In a non-randomized interventional model, efforts should be made to eliminate confounding effects through propensity score methods and the final propensity score-adjusted multivariable model may adjust any prognostic variables, while a randomized study simply should adjust any prognostic variables. 27 Maintaining the event per variable (EVR) is important to avoid overfitting in any type of modeling; therefore, screening of variables may be required in some association and explanatory studies, which may be accomplished using a backward stepwise method that needs to be clarified in the statistical analyses. 10 In a predictive study, a model with an optimum set of variables producing the highest accuracy should be used. The optimum set of variables may be screened with the random forest method or bootstrap or machine learning methods. 64 65 Different methods of variable selection and adjustments may lead to different results. The screening process of variables and their adjustments in the final multivariable model should be clearly mentioned in the statistical analysis section.

A study 66 evaluating the effect of hydroxychloroquine (HDQ) showed unfavorable events (intubation or death) in patients who received HDQ compared with those who did not (hazard ratio (HR): 2.37, 95% CI 1.84 to 3.02) in an unadjusted analysis. However, the propensity score-adjusted analyses as appropriate with the interventional objective model showed no significant association between HDQ use and unfavorable events (HR: 1.04, 95% CI 0.82 to 1.32), which was also confirmed in multivariable and other propensity score-adjusted analyses. This study clearly suggests that results interpretation should be based on a multivariable analysis only in observational studies if feasible. A recent study 10 noted that approximately 6% of multivariable analyses based on either logistic or Cox regression used an inappropriate selection method of variables in medical research. This practice was more commonly noted in studies that did not involve an expert biostatistician. Another review 61 of 316 articles from high-impact Chinese medical journals revealed that 30.7% of articles did not report the selection of variables in multivariable models. Indeed, this inappropriate practice could have been identified more commonly if classified according to the study objective type. 18 In RCTs, it is uncommon to report an adjusted analysis based on prognostic variables, even though an adjusted analysis may produce an efficient estimate compared with an unadjusted analysis. A study assessing the effect of preemptive intervention on development outcomes showed a significant effect of an intervention on reducing autism spectrum disorder symptoms. 67 However, this study was criticized by Ware 68 for not reporting non-significant results in unadjusted analyses. If possible, unadjusted estimates should also be reported in any study, particularly in RCTs. 23 68

Step 7: provide evidence for exploring effect modifiers (applicability)

Any variable that modifies the effect of exposure on the outcome is called an effect modifier or modifier or an interacting variable. Exploring the effect modifiers in multivariable analyses helps in (1) determining the applicability/generalizability of findings in the overall or specific subpopulation, (2) generating ideas for new hypotheses, (3) explaining uninterpretable findings between unadjusted and adjusted analyses, (4) guiding to present combined or separate models for each specific subpopulation, and (5) explaining heterogeneity in treatment effect. Often, investigators present adjusted stratified results according to the presence or absence of an effect modifier. If the exposure interacts with multiple variables statistically or conceptually in the model, then the stratified findings (subgroup) according to each effect modifier may be presented. Otherwise, stratified analysis substantially reduces the power of the study due to the lower sample size in each stratum and may produce significant results by inflating type I error. 69 Therefore, a multivariable analysis involving an interaction term as opposed to a stratified analysis may be presented in the presence of an effect modifier. 70 Sometimes, a quantitative variable may emerge as a potential effect modifier for exposure and an outcome relationship. In such a situation, the quantitative variable should not be categorized unless a clinically meaningful threshold is not available in the study. In fact, the practice of categorizing quantitative variables should be avoided in the analysis unless a clinically meaningful cut-off is available or a hypothesis requires for it. 71 In an exploratory objective type, any possible interaction may be obtained in a study; however, the interpretation should be guided based on clinical implications. Similarly, some objective models may have more than one exposure or intervention and the association of each exposure according to the level of other exposure should be presented through adjusted analyses as suggested in the presence of interaction effects. 70

A review of 428 articles from MEDLINE on the quality of reporting from statistical analyses of three (linear, logistic, and Cox) commonly used regression models reported that only 18.5% of the published articles provided interaction analyses, 17 even though interaction analyses can provide a lot of useful information.

Step 8: assessment of assumptions, specifically the distribution of outcome, linearity, multicollinearity, sparsity, and overfitting (reliability)

The assessment and reporting of model diagnostics are important in assessing the efficiency, validity, and usefulness of the model. Model diagnostics include satisfying model-specific assumptions and the assessment of sparsity, linearity, distribution of outcome, multicollinearity, and overfitting. 61 72 Model-specific assumptions such as normal residuals, heteroscedasticity and independence of errors in linear regression, proportionality in Cox regression, proportionality odds assumption in ordinal logistic regression, and distribution fit in other types of continuous and count models are required. In addition, sparsity should also be examined prior to selecting an appropriate model. Sparsity indicates many zero observations in the data set. 73 In the presence of sparsity, the effect size is difficult to interpret. Except for machine learning models, most of the parametric and semiparametric models require a linear relationship between independent variables and a functional form of an outcome. Linearity should be assessed using a multivariable polynomial in all model objectives. 62 Similarly, the appropriate choice of the distribution of outcome is required for model building in all study objective models. Multicollinearity assessment is also useful in all objective models. Assessment of EVR in multivariable analysis can be used to avoid the overfitting issue of a multivariable model. 18

Some review studies highlighted that 73.8%–92% of the articles published in MEDLINE had not assessed the model diagnostics of the multivariable regression models. 17 61 72 Contrary to the monotonically, linearly increasing relationship between systolic blood pressure (SBP) and mortality established using the Framingham’s study, 74 Port et al 75 reported a non-linear relationship between SBP and all-cause mortality or cardiovascular deaths by reanalysis of the Framingham’s study data set. This study identified a different threshold for treating hypertension, indicating the role of linearity assessment in multivariable models. Although a non-Gaussian distribution model may be required for modeling patient delay outcome data in cancer, 55 a study analyzed patient delay data using an ordinary linear regression model. 57 An investigation of the development of predictive models and their reporting in medical journals identified that 53% of the articles had fewer EVR than the recommended EVR, indicating over half of the published articles may have an overfitting model. 18 Another study 76 attempted to identify the anthropometric variables associated with non-insulin-dependent diabetes and found that none of the anthropometric variables were significant after adjusting for waist circumference, age, and sex, indicating the presence of collinearity. A study reported detailed sparse data problems in published studies and potential solutions. 73

Step 9: report type of primary and sensitivity analyses (consistency)

Numerous considerations and assumptions are made throughout the research processes that require assessment, evaluation, and validation. Some assumptions, executions, and errors made at the beginning of the study data collection may not be fixable 13 ; however, additional information collected during the study and data processing, including data distribution obtained at the end of the study, may facilitate additional considerations that need to be verified in the statistical analyses. Consistencies in the research findings via modifications in the outcome or exposure definition, study population, accounting for missing data, model-related assumptions, variables and their forms, and accounting for adherence to protocol in the models can be evaluated and reported in research studies using sensitivity analyses. 77 The purpose and type of supporting analyses need to be specified clearly in the statistical analyses to differentiate the main findings from the supporting findings. Sensitivity analyses are different from secondary or interim or subgroup analyses. 78 Data analyses for secondary outcomes are often referred to as secondary analyses, while data analyses of an ongoing study are called interim analyses and data analyses according to groups based on patient characteristics are known as subgroup analyses.

Almost all studies require some form of sensitivity analysis to validate the findings under different conditions. However, it is often underutilized in medical journals. Only 18%–20.3% of studies reported some forms of sensitivity analyses. 77 78 A review of nutritional trials from high-quality journals reflected that 17% of the conclusions were reported inappropriately using findings from sensitivity analyses not based on the primary/main analyses. 77

Step 10: provide methods for summarizing, displaying, and interpreting data (transparency and usability)

Data presentation includes data summary, data display, and data from statistical model analyses. The primary purpose of the data summary is to understand the distribution of outcome status and other characteristics in the total sample and by primary exposure status or outcome status. Column-wise data presentation should be preferred according to exposure status in all study designs, while row-wise data presentation for the outcome should be preferred in all study designs except for a case–control study. 24 32 Summary statistics should be used to provide maximum information on data distribution aligned with DGP and variable type. The purpose of results presentation primarily from regression analyses or statistical models is to convey results interpretation and implications of findings. The results should be presented according to the study objective type. Accordingly, the reporting of unadjusted and adjusted associations of each factor with the outcome may be preferred in the determinant objective model, while unadjusted and adjusted effects of primary exposure on the outcome may be preferred in the explanatory objective model. In prognostic models, the final predictive models may be presented in such a way that users can use models to predict an outcome. In the exploratory objective model, a final multivariable model should be reported with R 2 or area under the curve (AUC). In the association and interventional models, the assessment of internal validation is critically important through various sensitivity and validation analyses. A model with better fit indices (in terms of R 2 or AUC, Akaike information criterion, Bayesian information criterion, fit index, root mean square error) should be finalized and reported in the causal model objective study. In the predictive objective type, the model performance in terms of R 2 or AUC in training and validation data sets needs to be reported ( figure 1 ). 20 21 There are multiple purposes of data display, including data distribution using bar diagram or histogram or frequency polygons or box plots, comparisons using cluster bar diagram or scatter dot plot or stacked bar diagram or Kaplan-Meier plot, correlation or model assessment using scatter plot or scatter matrix, clustering or pattern using heatmap or line plots, the effect of predictors with fitted models using marginsplot, and comparative evaluation of effect sizes from regression models using forest plot. Although the key purpose of data display is to highlight critical issues or findings in the study, data display should essentially follow DGP and variable types and should be user-friendly. 54 79 Data interpretation heavily relies on the effect size measure along with study design and specified hypotheses. Sometimes, variables require standardization for descriptive comparison of effect sizes among exposures or interpreting small effect size, or centralization for interpreting intercept or avoiding collinearity due to interaction terms, or transformation for achieving model-related assumptions. 80 Appropriate methods of data reporting and interpretation aligned with study design, study hypothesis, and effect size measure should be specified in the statistical analysis section of research studies.

Published articles from reputed journals inappropriately summarized a categorized variable with mean and range, 81 summarized a highly skewed variable with mean and standard deviation, 57 and treated a categorized variable as a continuous variable in regression analyses. 82 Similarly, numerous examples from published studies reporting inappropriate graphical display or inappropriate interpretation of data not aligned with DGP or variable types are illustrated in a book published by Bland and Peacock. 83 84 A study used qualitative data on MRI but inappropriately presented with a Box-Whisker plot. 81 Another study reported unusually high OR for an association between high breast parenchymal enhancement and breast cancer in both premenopausal and postmenopausal women. 85 This reporting makes suspicious findings and may include sparse data bias. 86 A poor tabular presentation without proper scaling or standardization of a variable, missing CI for some variables, missing unit and sample size, and inconsistent reporting of decimal places could be easily noticed in table 4 of a published study. 29 Some published predictive models 87 do not report intercept or baseline survival estimates to use their predictive models in clinical use. Although a direct comparison of effect sizes obtained from the same model may be avoided if the units are different among variables, 35 a study had an objective to compare effect sizes across variables but the authors performed comparisons without standardization of variables or using statistical tests. 88

A sample for writing statistical analysis section in medical journals/research studies

Our primary study objective type was to develop a (select from figure 1 ) model to assess the relationship of risk factors (list critical variables or exposures) with outcomes (specify type from continuous/discrete/count/binary/polytomous/time-to-event). To address this objective, we conducted a (select from figure 2 or any other) study design to test the hypotheses of (equality or superiority or non-inferiority or equivalence or futility) or develop prediction. Accordingly, the other variables were adjusted or considered as (specify role of variables from confounders, covariates, or predictors or independent variables) as reflected in the conceptual framework. In the unadjusted or preliminary analyses as per the (select from figure 3 or any other design features) DGP, (specify EBB preferred tests from online supplemental table 3 or any other appropriate tests) were used for (specify variables and types) in unadjusted analyses. According to the EBB practice for the outcome (specify type) and DGP of (select from figure 3 or any other), we used (select from online supplemental table 1 or specify a multivariable approach) as the primary model in the multivariable analysis. We used (select from figure 1 ) variable selection method in the multivariable analysis and explored the interaction effects between (specify variables). The model diagnostics including (list all applicable, including model-related assumptions, linearity, or multicollinearity or overfitting or distribution of outcome or sparsity) were also assessed using (specify appropriate methods) respectively. In such exploration, we identified (specify diagnostic issues if any) and therefore the multivariable models were developed using (specify potential methods used to handle diagnostic issues). The other outcomes were analyzed with (list names of multivariable approaches with respective outcomes). All the models used the same procedure (or specify from figure 1 ) for variable selection, exploration of interaction effects, and model diagnostics using (specify statistical approaches) depending on the statistical models. As per the study design, hypothesis, and multivariable analysis, the results were summarized with effect size (select as appropriate or from figure 2 ) along with (specify 95% CI or other interval estimates) and considered statistically significant using (specify the side of p value or alternatives) at (specify the level of significance) due to (provide reasons for choosing a significance level). We presented unadjusted and/or adjusted estimates of primary outcome according to (list primary exposures or variables). Additional analyses were conducted for (specific reasons from step 9) using (specify methods) to validate findings obtained in the primary analyses. The data were summarized with (list summary measures and appropriate graphs from step 10), whereas the final multivariable model performance was summarized with (fit indices if applicable from step 10). We also used (list graphs) as appropriate with DGP (specify from figure 3 ) to present the critical findings or highlight (specify data issues) using (list graphs/methods) in the study. The exposures or variables were used in (specify the form of the variables) and therefore the effect or association of (list exposures or variables) on outcome should be interpreted in terms of changes in (specify interpretation unit) exposures/variables. List all other additional analyses if performed (with full details of all models in a supplementary file along with statistical codes if possible).

Concluding remarks

We highlighted 10 essential steps to be reported in the statistical analysis section of any analytical study ( figure 4 ). Adherence to minimum reporting of the steps specified in this report may enforce investigators to understand concepts and approach biostatisticians timely to apply these concepts in their study to improve the overall quality of methodological standards in grant proposals and research studies. The order of reporting information in statistical analyses specified in this report is not mandatory; however, clear reporting of analytical steps applicable to the specific study type should be mentioned somewhere in the manuscript. Since the entire approach of statistical analyses is dependent on the study objective type and EBB practice, proper execution and reporting of statistical models can be taught to the next generation of statisticians by the study objective type in statistical education courses. In fact, some disciplines ( figure 5 ) are strictly aligned with specific study objective types. Bioinformaticians are oriented in studying determinant and prognostic models toward precision medicine, while epidemiologists are oriented in studying association and causal models, particularly in population-based observational and pragmatic settings. Data scientists are heavily involved in prediction and classification models in personalized medicine. A common thing across disciplines is using biostatistical principles and computation tools to address any research question. Sometimes, one discipline expert does the part of others. 89 We strongly recommend using a team science approach that includes an epidemiologist, biostatistician, data scientist, and bioinformatician depending on the study objectives and needs. Clear reporting of data analyses as per the study objective type should be encouraged among all researchers to minimize heterogeneous practices and improve scientific quality and outcomes. In addition, we also encourage investigators to strictly follow transparent reporting and quality assessment guidelines according to the study design ( https://www.equator-network.org/ ) to improve the overall quality of the study, accordingly STROBE (Strengthening the Reporting of Observational Studies in Epidemiology) for observational studies, CONSORT (Consolidated Standards of Reporting Trials) for clinical trials, STARD (Standards for Reporting Diagnostic Accuracy Studies) for diagnostic studies, TRIPOD (Transparent Reporting of a multivariable prediction model for Individual Prognosis OR Diagnosis) for prediction modeling, and ARRIVE (Animal Research: Reporting of In Vivo Experiments) for preclinical studies. The steps provided in this document for writing the statistical analysis section is essentially different from other guidance documents, including SAMBR. 5 SAMBR provides a guidance document for selecting evidence-based preferred methods of statistical analysis according to different study designs, while this report suggests the global reporting of essential information in the statistical analysis section according to study objective type. In this guidance report, our suggestion strictly pertains to the reporting of methods in the statistical analysis section and their implications on the interpretation of results. Our document does not provide guidance on the reporting of sample size or results or statistical analysis section for meta-analysis. The examples and reviews reported in this study may be used to emphasize the concepts and related implications in medical research.

Summary of reporting steps, purpose, and evaluation measures in the statistical analysis section.

Role of interrelated disciplines according to study objective type.

Acknowledgments

The author would like to thank the reviewers for their careful review and insightful suggestions.

Contributors: AKD developed the concept and design and wrote the manuscript.

Funding: The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests: AKD is a Journal of Investigative Medicine Editorial Board member. No other competing interests declared.

Provenance and peer review: Commissioned; externally peer reviewed.

Supplemental material: This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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• Volume 58, Issue 17
• Where is the research on sport-related concussion in Olympic athletes? A descriptive report and assessment of the impact of access to multidisciplinary care on recovery
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• http://orcid.org/0000-0002-3298-5719 Thomas Romeas 1 , 2 , 3 ,
• http://orcid.org/0000-0003-1748-7241 Félix Croteau 3 , 4 , 5 ,
• Suzanne Leclerc 3 , 4
• 1 Sport Sciences , Institut national du sport du Québec , Montreal , Quebec , Canada
• 2 School of Optometry , Université de Montréal , Montreal , Quebec , Canada
• 3 IOC Research Centre for Injury Prevention and Protection of Athlete Health , Réseau Francophone Olympique de la Recherche en Médecine du Sport , Montreal , Quebec , Canada
• 4 Sport Medicine , Institut national du sport du Québec , Montreal , Quebec , Canada
• 5 School of Physical and Occupational Therapy , McGill University , Montreal , Quebec , Canada
• Correspondence to Dr Thomas Romeas; thomas.romeas{at}umontreal.ca

Objectives This cohort study reported descriptive statistics in athletes engaged in Summer and Winter Olympic sports who sustained a sport-related concussion (SRC) and assessed the impact of access to multidisciplinary care and injury modifiers on recovery.

Methods 133 athletes formed two subgroups treated in a Canadian sport institute medical clinic: earlier (≤7 days) and late (≥8 days) access. Descriptive sample characteristics were reported and unrestricted return to sport (RTS) was evaluated based on access groups as well as injury modifiers. Correlations were assessed between time to RTS, history of concussions, the number of specialist consults and initial symptoms.

Results 160 SRC (median age 19.1 years; female=86 (54%); male=74 (46%)) were observed with a median (IQR) RTS duration of 34.0 (21.0–63.0) days. Median days to care access was different in the early (1; n SRC =77) and late (20; n SRC =83) groups, resulting in median (IQR) RTS duration of 26.0 (17.0–38.5) and 45.0 (27.5–84.5) days, respectively (p<0.001). Initial symptoms displayed a meaningful correlation with prognosis in this study (p<0.05), and female athletes (52 days (95% CI 42 to 101)) had longer recovery trajectories than male athletes (39 days (95% CI 31 to 65)) in the late access group (p<0.05).

Conclusions Olympic athletes in this cohort experienced an RTS time frame of about a month, partly due to limited access to multidisciplinary care and resources. Earlier access to care shortened the RTS delay. Greater initial symptoms and female sex in the late access group were meaningful modifiers of a longer RTS.

• Brain Concussion
• Cohort Studies
• Retrospective Studies

Data availability statement

Data are available on reasonable request. Due to the confidential nature of the dataset, it will be shared through a controlled access repository and made available on specific and reasonable requests.

https://doi.org/10.1136/bjsports-2024-108211

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WHAT IS ALREADY KNOWN ON THIS TOPIC

Most data regarding the impact of sport-related concussion (SRC) guidelines on return to sport (RTS) are derived from collegiate or recreational athletes. In these groups, time to RTS has steadily increased in the literature since 2005, coinciding with the evolution of RTS guidelines. However, current evidence suggests that earlier access to care may accelerate recovery and RTS time frames.

WHAT THIS STUDY ADDS

This study reports epidemiological data on the occurrence of SRC in athletes from several Summer and Winter Olympic sports with either early or late access to multidisciplinary care. We found the median time to RTS for Olympic athletes with an SRC was 34.0 days which is longer than that reported in other athletic groups such as professional or collegiate athletes. Time to RTS was reduced by prompt access to multidisciplinary care following SRC, and sex-influenced recovery in the late access group with female athletes having a longer RTS timeline. Greater initial symptoms, but not prior concussion history, were also associated with a longer time to RTS.

HOW THIS STUDY MIGHT AFFECT RESEARCH, PRACTICE OR POLICY

Considerable differences exist in access to care for athletes engaged in Olympic sports, which impact their recovery. In this cohort, several concussions occurred during international competitions where athletes are confronted with poor access to organised healthcare. Pathways for prompt access to multidisciplinary care should be considered by healthcare authorities, especially for athletes who travel internationally and may not have the guidance or financial resources to access recommended care.

Introduction

After two decades of consensus statements, sport-related concussion (SRC) remains a high focus of research, with incidence ranging from 0.1 to 21.5 SRC per 1000 athlete exposures, varying according to age, sex, sport and level of competition. 1 2 Evidence-based guidelines have been proposed by experts to improve its identification and management, such as those from the Concussion in Sport Group. 3 Notably, they recommend specific strategies to improve SRC detection and monitoring such as immediate removal, 4 prompt access to healthcare providers, 5 evidence-based interventions 6 and multidisciplinary team approaches. 7 It is believed that these guidelines contribute to improving the early identification and management of athletes with an SRC, thereby potentially mitigating its long-term consequences.

Nevertheless, evidence regarding the impact of SRC guidelines implementation remains remarkably limited, especially within high-performance sport domains. In fact, most reported SRC data focus on adolescent student-athletes, collegiate and sometimes professional athletes in the USA but often neglect Olympians. 1 2 8–11 Athletes engaged in Olympic sports, often referred to as elite amateurs, are typically classified among the highest performers in elite sport, alongside professional athletes. 12 13 They train year-round and uniquely compete regularly on the international stage in sports that often lack professional leagues and rely on highly variable resources and facilities, mostly dependent on winning medals. 14 Unlike professional athletes, Olympians do not have access to large financial rewards. Although some Olympians work or study in addition to their intensive sports practice, they can devote more time to full-time sports practice compared with collegiate athletes. Competition calendars in Olympians differ from collegiate athletes, with periodic international competitions (eg, World Cups, World Championships) throughout the whole year rather than regular domestic competitions within a shorter season (eg, semester). Olympians outclass most collegiate athletes, and only the best collegiate athletes will have the chance to become Olympians and/or professionals. 12 13 15 In Canada, a primary reason for limited SRC data in Olympic sports is that the Canadian Olympic and Paralympic Sports Institute (COPSI) network only adopted official guidelines in 2018 to standardise care for athletes’ SRC nationwide. 16 17 The second reason could be the absence of a centralised medical structure and surveillance systems, identified as key factors contributing to the under-reporting and underdiagnosis of athletes with an SRC. 18

Among the available evidence on the evolution of SRC management, a 2023 systematic review and meta-analysis in athletic populations including children, adolescents and adults indicated that a full return to sport (RTS) could take up to a month but is estimated to require 19.8 days on average (15.4 days in adults), as opposed to the initial expectation of approximately 10.0 days based on studies published prior to 2005. 19 In comparison, studies focusing strictly on American collegiate athletes report median times to RTS of 16 days. 9 20 21 Notably, a recent study of military cadets reported an even longer return to duty times of 29.4 days on average, attributed to poorer access to care and fewer incentives to return to play compared with elite sports. 22 In addition, several modifiers have also been identified as influencing the time to RTS, such as the history of concussions, type of sport, sex, past medical problems (eg, preinjury modifiers), as well as the initial number of symptoms and their severity (eg, postinjury modifiers). 20 22 The evidence regarding the potential influence of sex on the time to RTS has yielded mixed findings in this area. 23–25 In fact, females are typically under-represented in SRC research, highlighting the need for additional studies that incorporate more balanced sample representation across sexes and control for known sources of bias. 26 Interestingly, a recent Concussion Assessment, Research and Education Consortium study, which included a high representation of concussed female athletes (615 out of 1071 patients), revealed no meaningful differences in RTS between females and males (13.5 and 11.8 days, respectively). 27 Importantly, findings in the sporting population suggested that earlier initiation of clinical care is linked to shorter recovery after concussion. 5 28 However, these factors affecting the time to RTS require a more thorough investigation, especially among athletes engaged in Olympic sports who may or may not have equal access to prompt, high-quality care.

Therefore, the primary objective of this study was to provide descriptive statistics among athletes with SRC engaged in both Summer and Winter Olympic sport programmes over a quadrennial, and to assess the influence of recommended guidelines of the COPSI network and the fifth International Consensus Conference on Concussion in Sport on the duration of RTS performance. 16 17 Building on available evidence, the international schedule constraints, variability in resources 14 and high-performance expectation among this elite population, 22 prolonged durations for RTS, compared with what is typically reported (eg, 16.0 or 15.4 days), were hypothesised in Olympians. 3 19 The secondary objective was to more specifically evaluate the impact of access to multidisciplinary care and injury modifiers on the time to RTS. Based on current evidence, 5 7 29 30 the hypothesis was formulated that athletes with earlier multidisciplinary access would experience a faster RTS. Regarding injury modifiers, it was expected that female and male athletes would show similar time to RTS despite presenting sex-specific characteristics of SRC. 31 The history of concussions, the severity of initial symptoms and the number of specialist consults were expected to be positively correlated to the time to RTS. 20 32

Participants

A total of 133 athletes (F=72; M=61; mean age±SD: 20.7±4.9 years old) who received medical care at the Institut national du sport du Québec, a COPSI training centre set up with a medical clinic, were included in this cohort study with retrospective analysis. They participated in 23 different Summer and Winter Olympic sports which were classified into six categories: team (soccer, water polo), middle distance/power (rowing, swimming), speed/strength (alpine skiing, para alpine skiing, short and long track speed skating), precision/skill-dependent (artistic swimming, diving, equestrian, figure skating, gymnastics, skateboard, synchronised skating, trampoline) and combat/weight-making (boxing, fencing, judo, para judo, karate, para taekwondo, wrestling) sports. 13 This sample consists of two distinct groups: (1) early access group in which athletes had access to a medical integrated support team of multidisciplinary experts within 7 days following their SRC and (2) late access group composed of athletes who had access to a medical integrated support team of multidisciplinary experts eight or more days following their SRC. 5 30 Inclusion criteria for the study were participation in a national or international-level sports programme 13 and having sustained at least one SRC diagnosed by an authorised healthcare practitioner (eg, physician and/or physiotherapist).

Clinical context

The institute clinic provides multidisciplinary services for care of patients with SRC including a broad range of recommended tests for concussion monitoring ( table 1 ). The typical pathway for the athletes consisted of an initial visit to either a sports medicine physician or their team sports therapist. A clinical diagnosis of SRC was then confirmed by a sports medicine physician, and referral for the required multidisciplinary assessments ensued based on the patient’s signs and symptoms. Rehabilitation progression was based on the evaluation of exercise tolerance, 33 priority to return to cognitive tasks and additional targeted support based on clinical findings of a cervical, visual or vestibular nature. 17 The expert team worked in an integrated manner with the athlete and their coaching staff for the rehabilitation phase, including regular round tables and ongoing communication. 34 For some athletes, access to recommended care was fee based, without a priori agreements with a third party payer (eg, National Sports Federation).

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Main evaluations performed to guide the return to sport following sport-related concussion

Data collection

Data were collected at the medical clinic using a standardised injury surveillance form based on International Olympic Committee guidelines. 35 All injury characteristics were extracted from the central injury database between 1 July 2018 and 31 July 2022. This period corresponds to a Winter Olympic sports quadrennial but also covers 3 years for Summer Olympic sports due to the postponing of the Tokyo 2020 Olympic Games. Therefore, the observation period includes a typical volume of competitions across sports and minimises differences in exposure based on major sports competition schedules. The information extracted from the database included: participant ID, sex, date of birth, sport, date of injury, type of injury, date of their visit at the clinic, clearance date of unrestricted RTS (eg, defined as step 6 of the RTS strategy with a return to normal gameplay including competitions), the number and type of specialist consults, mechanism of injury (eg, fall, hit), environment where the injury took place (eg, training, competition), history of concussions, history of modifiers (eg, previous head injury, migraines, learning disability, attention deficit disorder or attention deficit/hyperactivity disorder, depression, anxiety, psychotic disorder), as well as the number of symptoms and the total severity score from the first Sport Concussion Assessment Tool 5 (SCAT5) assessment following SRC. 17

Following a Shapiro-Wilk test, medians, IQR and non-parametric tests were used for the analyses because of the absence of normal distributions for all the variables in the dataset (all p<0.001). The skewness was introduced by the presence of individuals that required lengthy recovery periods. One participant was removed from the analysis because their time to consult with the multidisciplinary team was extremely delayed (>1 year).

Descriptive statistics were used to describe the participant’s demographics, SRC characteristics and risk factors in the total sample. Estimated incidences of SRC were also reported for seven resident sports at the institute for which it was possible to quantify a detailed estimate of training volume based on the annual number of training and competition hours as well as the number of athletes in each sport.

To assess if access to multidisciplinary care modified the time to RTS, we compared time to RTS between early and late access groups using a method based on median differences described elsewhere. 36 Wilcoxon rank sum tests were also performed to make between-group comparisons on single variables of age, time to first consult, the number of specialists consulted and medical visits. Fisher’s exact tests were used to compare count data between groups on variables of sex, history of concussion, time since the previous concussion, presence of injury modifiers, environment and mechanism of injury. Bonferroni corrections were applied for multiple comparisons in case of meaningful differences.

To assess if injury modifiers modified time to RTS in the total sample, we compared time to RTS between sexes, history of concussions, time since previous concussion or other injury modifiers using a method based on median differences described elsewhere. 36 Kaplan-Meier curves were drawn to illustrate time to RTS differences between sexes (origin and start time: date of injury; end time: clearance date of unrestricted RTS). Trajectories were then assessed for statistical differences using Cox proportional hazards model. Wilcoxon rank sum tests were employed for comparing the total number of symptoms and severity scores on the SCAT5. The association of multilevel variables on return to play duration was evaluated in the total sample with Kruskal-Wallis rank tests for environment, mechanism of injury, history of concussions and time since previous concussion. For all subsequent analyses of correlations between SCAT5 results and secondary variables, only data obtained from SCAT5 assessments within the acute phase of injury (≤72 hours) were considered (n=65 SRC episodes in the early access group). 37 Spearman rank correlations were estimated between RTS duration, history of concussions, number of specialist consults and total number of SCAT5 symptoms or total symptom severity. All statistical tests were performed using RStudio (R V.4.1.0, The R Foundation for Statistical Computing). The significance level was set to p<0.05.

Equity, diversity and inclusion statement

The study population is representative of the Canadian athletic population in terms of age, gender, demographics and includes a balanced representation of female and male athletes. The study team consists of investigators from different disciplines and countries, but with a predominantly white composition and under-representation of other ethnic groups. Our study population encompasses data from the Institut national du sport du Québec, covering individuals of all genders, ethnicities and geographical regions across Canada.

Patient and public involvement

The patients or the public were not involved in the design, conduct, reporting or dissemination plans of our research.

Sample characteristics

During the 4-year period covered by this retrospective chart review, a total of 160 SRC episodes were recorded in 132 athletes with a median (IQR) age of 19.1 (17.8–22.2) years old ( table 2 ). 13 female and 10 male athletes had multiple SRC episodes during this time. The sample had a relatively balanced number of females (53.8%) and males (46.2%) with SRC included. 60% of the sample reported a history of concussion, with 35.0% reporting having experienced more than two episodes. However, most of these concussions had occurred more than 1 year before the SRC for which they were being treated. Within this sample, 33.1% of participants reported a history of injury modifiers. Importantly, the median (IQR) time to first clinic consult was 10.0 (1.0–20.0) days and the median (IQR) time to RTS was 34.0 (21.0–63.0) days in this sample ( table 3 ). The majority of SRCs occurred during training (56.3%) rather than competition (33.1%) and were mainly due to a fall (63.7%) or a hit (31.3%). The median (IQR) number of follow-up consultations and specialists consulted after the SRC were, respectively, 9 (5.0–14.3) and 3 (2.0–4.0).

Participants demographics

Sport-related concussion characteristics

Among seven sports of the total sample (n=89 SRC), the estimated incidence of athletes with SRC was highest in short-track speed skating (0.47/1000 hours; 95% CI 0.3 to 0.6), and lower in boxing, trampoline, water polo, judo, artistic swimming, and diving (0.24 (95% CI 0.0 to 0.5), 0.16 (95% CI 0.0 to 0.5), 0.13 (95% CI 0.1 to 0.2), 0.11 (95% CI 0.1 to 0.2), 0.09 (95% CI 0.0 to 0.2) and 0.06 (95% CI 0.0 to 0.1)/1000, respectively ( online supplemental material ). Furthermore, most athletes sustained an SRC in training (66.5%; 95% CI 41.0 to 92.0) rather than competition (26.0%; 95% CI 0.0 to 55.0) except for judo athletes (20.0% (95% CI 4.1 to 62.0) and 80.0% (95% CI 38.0 to 96.0), respectively). Falls were the most common injury mechanism in speed skating, trampoline and judo while hits were the most common injury mechanism in boxing, water polo, artistic swimming and diving.

Supplemental material

Access to care.

The median difference in time to RTS was 19 days (95% CI 9.3 to 28.7; p<0.001) between the early (26 (IQR 17.0–38.5) days) and late (45 (IQR 27.5–84.5) days) access groups ( table 3 ; figure 1 ). Importantly, the distribution of SRC environments was different between both groups (p=0.008). The post hoc analysis demonstrated a meaningful difference in the distribution of SRC in training and competition environments between groups (p=0.029) but not for the other comparisons. There was a meaningful difference between the groups in time to first consult (p<0.001; 95% CI −23.0 to −15.0), but no meaningful differences between groups in median age (p=0.176; 95% CI −0.3 to 1.6), sex distribution (p=0.341; 95% CI 0.7 to 2.8), concussion history (p=0.210), time since last concussion (p=0.866), mechanisms of SRC (p=0.412), the presence of modifiers (p=0.313; 95% CI 0.3 to 1.4) and the number of consulted specialists (p=0.368; 95% CI −5.4 to 1.0) or medical visits (p=0.162; 95% CI −1.0 to 3.0).

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Time to return to sport following sport-related concussion as a function of group’s access to care and sex. Outliers: below=Q1−1.5×IQR; above=Q3+1.5×IQR.

The median difference in time to RTS was 6.5 days (95% CI −19.3 to 5.3; p=0.263; figure 1 ) between female (37.5 (IQR 22.0–65.3) days) and male (31.0 (IQR 20.0–48.0) days) athletes. Survival analyses highlighted an increased hazard of longer recovery trajectory in female compared with male athletes (HR 1.4; 95% CI 1.4 to 0.7; p=0.052; figure 2A ), which was mainly driven by the late (HR 1.8; 95% CI 1.8 to 0.6; p=0.019; figure 2C ) rather than the early (HR 1.1; 95% CI 1.1 to 0.9; p=0.700; figure 2B ) access group. Interestingly, a greater number of female athletes (n=15) required longer than 100 days for RTS as opposed to the male athletes (n=6). There were no meaningful differences between sexes for the total number of symptoms recorded on the SCAT5 (p=0.539; 95% CI −1.0 to 2.0) nor the total symptoms total severity score (p=0.989; 95% CI −5.0 to 5.0).

Time analysis of sex differences in the time to return to sport following sport-related concussion in the (A) total sample, as well as (B) early, and (C) late groups using survival curves with 95% confidence bands and tables of time-specific number of patients at risk (censoring proportion: 0%).

History of modifiers

SRC modifiers are presented in table 2 , and their influence on RTP is shown in table 4 . The median difference in time to RTS was 1.5 days (95% CI −10.6 to 13.6; p=0.807) between athletes with none and one episode of previous concussion, was 3.5 days (95% CI −13.9 to 19.9; p=0.728) between athletes with none and two or more episodes of previous concussion, and was 2 days (95% CI −12.4 to 15.4; p=0.832) between athletes with one and two or more episodes of previous concussion. The history of concussions (none, one, two or more) had no meaningful impact on the time to RTS (p=0.471). The median difference in time to RTS was 4.5 days (95% CI −21.0 to 30.0; p=0.729) between athletes with none and one episode of concussion in the previous year, was 2 days (95% CI −10.0 to 14.0; p=0.744) between athletes with none and one episode of concussion more than 1 year ago, and was 2.5 days (95% CI −27.7 to 22.7; p=0.846) between athletes with an episode of concussion in the previous year and more than 1 year ago. Time since the most recent concussion did not change the time to RTS (p=0.740). The longest time to RTS was observed in the late access group in which athletes had a concussion in the previous year, with a very large spread of durations (65.0 (IQR 33.0–116.5) days). The median difference in time to RTS was 3 days (95% CI −13.1 to 7.1; p=0.561) between athletes with and without other injury modifiers. The history of other injury modifiers had no meaningful influence on the time to RTS (95% CI −6.0 to 11.0; p=0.579).

Preinjury modifiers of time to return to sport following SRC

SCAT5 symptoms and severity scores

Positive associations were observed between the time to RTS and the number of initial symptoms (r=0.3; p=0.010; 95% CI 0.1 to 0.5) or initial severity score (r=0.3; p=0.008; 95% CI 0.1 to 0.5) from the SCAT5. The associations were not meaningful between the number of specialist consultations and the initial number of symptoms (r=−0.1; p=0.633; 95% CI −0.3 to 0.2) or initial severity score (r=−0.1; p=0.432; 95% CI −0.3 to 0.2). Anecdotally, most reported symptoms following SRC were ‘headache’ (86.2%) and ‘pressure in the head’ (80.0%), followed by ‘fatigue’ (72.3%), ‘neck pain’ (70.8%) and ‘not feeling right’ (67.7%; online supplemental material ).

This study is the first to report descriptive data on athletes with SRC collected across several sports during an Olympic quadrennial, including athletes who received the most recent evidence-based care at the time of data collection. Primarily, results indicate that the time to RTS in athletes engaged in Summer and Winter Olympic sports may require a median (IQR) of 34.0 (21.0–63.0) days. Importantly, findings demonstrated that athletes with earlier (≤7 days) access to multidisciplinary concussion care showed faster RTS compared with those with late access. Time to RTS exhibited large variability where sex had a meaningful influence on the recovery pathway in the late access group. Initial symptoms, but not history of concussion, were correlated with prognosis in this sample. The main reported symptoms were consistent with previous studies. 38 39

Time to RTS in Olympic sports

This study provides descriptive data on the impact of SRC monitoring programmes on recovery in elite athletes engaged in Olympic sports. As hypothesised, the median time to RTS found in this study (eg, 34.0 days) was about three times longer than those found in reports from before 2005, and 2 weeks longer than the typical median values (eg, 19.8 days) recently reported in athletic levels including youth (high heterogeneity, I 2 =99.3%). 19 These durations were also twice as long as the median unrestricted time to RTS observed among American collegiate athletes, which averages around 16 days. 9 20 21 However, they were more closely aligned with findings from collegiate athletes with slow recovery (eg, 34.7 days) and evidence from military cadets with poor access where return to duty duration was 29.4 days. 8 22 Several reasons could explain such extended time to RTS, but the most likely seems to be related to the diversity in access among these sports to multidisciplinary services (eg, 10.0 median days (1–20)), well beyond the delays experienced by collegiate athletes, for example (eg, 0.0 median days (0–2)). 40 In the total sample, the delays to first consult with the multidisciplinary clinic were notably mediated by the group with late access, whose athletes had more SRC during international competition. One of the issues for athletes engaged in Olympic sports is that they travel abroad year-round for competitions, in contrast with collegiate athletes who compete domestically. These circumstances likely make access to quality care very variable and make the follow-up of care less centralised. Also, access to resources among these sports is highly variable (eg, medal-dependant), 14 and at the discretion of the sport’s leadership (eg, sport federation), who may decide to prioritise more or fewer resources to concussion management considering the relatively low incidence of this injury. Another explanation for the longer recovery times in these athletes could be the lack of financial incentives to return to play faster, which are less prevalent among Olympic sports compared with professionals. However, the stakes of performance and return to play are still very high among these athletes.

Additionally, it is plausible that studies vary their outcome with shifting operational definitions such as resolution of symptoms, return to activities, graduated return to play or unrestricted RTS. 19 40 It is understood that resolution of symptoms may occur much earlier than return to preinjury performance levels. Finally, an aspect that has been little studied to date is the influence of the sport’s demands on the RTS. For example, acrobatic sports requiring precision/technical skills such as figure skating, trampoline and diving, which involve high visuospatial and vestibular demands, 41 might require more time to recover or elicit symptoms for longer times. Anecdotally, athletes who experienced a long time to RTS (>100 days) were mostly from precision/skill-dependent sports in this sample. The sports demand should be further considered as an injury modifier. More epidemiological reports that consider the latest guidelines are therefore necessary to gain a better understanding of the true time to RTS and impact following SRC in Olympians.

Supporting early multidisciplinary access to care

In this study, athletes who obtained early access to multidisciplinary care after SRC recovered faster than those with late access to multidisciplinary care. This result aligns with findings showing that delayed access to a healthcare practitioner delays recovery, 19 including previous evidence in a sample of patients from a sports medicine clinic (ages 12–22), indicating that the group with a delayed first clinical visit (eg, 8–20 days) was associated with a 5.8 times increased likelihood of a recovery longer than 30 days. 5 Prompt multidisciplinary approach for patients with SRC is suggested to yield greater effectiveness over usual care, 3 6 17 which is currently evaluated under randomised controlled trial. 42 Notably, early physical exercise and prescribed exercise (eg, 48 hours postinjury) are effective in improving recovery compared with strict rest or stretching. 43 44 In fact, preclinical and clinical studies have shown that exercise has the potential to improve neurotransmission, neuroplasticity and cerebral blood flow which supports that the physically trained brain enhanced recovery. 45 46 Prompt access to specialised healthcare professionals can be challenging in some contexts (eg, during international travel), and the cost of accessing medical care privately may prove further prohibitive. This barrier to recovery should be a priority for stakeholders in Olympic sports and given more consideration by health authorities.

Estimated incidences and implications

The estimated incidences of SRC were in the lower range compared with what is reported in other elite sport populations. 1 2 However, the burden of injury remained high for these sports, and the financial resources as well as expertise required to facilitate athletes’ rehabilitation was considerable (median number of consultations: 9.0). Notably, the current standard of public healthcare in Canada does not subsidise the level of support recommended following SRC as first-line care, and the financial subsidisation of this recommended care within each federation is highly dependent on the available funding, varying significantly between sports. 14 Therefore, the ongoing efforts to improve education, prevention and early recognition, modification of rules to make the environments safer and multidisciplinary care access for athletes remain crucial. 7

Strength and limitations

This unique study provides multisport characteristics following the evolution of concussion guidelines in Summer and Winter Olympic sports in North America. Notably, it features a balance between the number of female and male athletes, allowing the analysis of sex differences. 23 26 In a previous review of 171 studies informing consensus statements, samples were mostly composed of more than 80% of male participants, and more than 40% of these studies did not include female participants at all. 26 This study also included multiple non-traditional sports typically not encompassed in SRC research, feature previously identified as a key requirement of future epidemiological research. 47

However, it must be acknowledged that potential confounding factors could influence the results. For example, the number of SRC detected during the study period does not account for potentially unreported concussions. Nevertheless, this figure should be minimal because these athletes are supervised both in training and in competition by medical staff. Next, the sport types were heterogeneous, with inconsistent risk for head impacts or inconsistent sport demand which might have an influence on recovery. Furthermore, the number of participants or sex in each sport was not evenly distributed, with short-track speed skaters representing a large portion of the overall sample (32.5%), for example. Additionally, the number of participants with specific modifiers was too small in the current sample to conclude whether the presence of precise characteristics (eg, history of concussion) impacted the time to RTS. Also, the group with late access was more likely to consist of athletes who sought specialised care for persistent symptoms. These complex cases are often expected to require additional time to recover. 48 Furthermore, athletes in the late group may have sought support outside of the institute medical clinic, without a coordinated multidisciplinary approach. Therefore, the estimation of clinical consultations was tentative for this group and may represent a potential confounding factor in this study.

This is the first study to provide evidence of the prevalence of athletes with SRC and modifiers of recovery in both female and male elite-level athletes across a variety of Summer and Winter Olympic sports. There was a high variability in access to care in this group, and the median (IQR) time to RTS following SRC was 34.0 (21.0–63.0) days. Athletes with earlier access to multidisciplinary care took nearly half the time to RTS compared with those with late access. Sex had a meaningful influence on the recovery pathway in the late access group. Initial symptom number and severity score but not history of concussion were meaningful modifiers of recovery. Injury surveillance programmes targeting national sport organisations should be prioritised to help evaluate the efficacy of recommended injury monitoring programmes and to help athletes engaged in Olympic sports who travel a lot internationally have better access to care. 35 49

Ethics statements

Patient consent for publication.

Not applicable.

Ethics approval

This study involves human participants and was approved by the ethics board of Université de Montréal (certificate #2023-4052). Participants gave informed consent to participate in the study before taking part.

Acknowledgments

The authors would like to thank the members of the concussion interdisciplinary clinic of the Institut national du sport du Québec for collecting the data and for their unconditional support to the athletes.

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Supplementary materials

Supplementary data.

This web only file has been produced by the BMJ Publishing Group from an electronic file supplied by the author(s) and has not been edited for content.

• Data supplement 1

X @ThomasRomeas

Correction notice This article has been corrected since it published Online First. The ORCID details have been added for Dr Croteau.

Contributors TR, FC and SL were involved in planning, conducting and reporting the work. François Bieuzen and Magdalena Wojtowicz critically reviewed the manuscript. TR is guarantor.

Funding The authors have not declared a specific grant for this research from any funding agency in the public, commercial or not-for-profit sectors.

Competing interests None declared.

Patient and public involvement Patients and/or the public were not involved in the design, or conduct, or reporting, or dissemination plans of this research.

Provenance and peer review Not commissioned; externally peer reviewed.

Supplemental material This content has been supplied by the author(s). It has not been vetted by BMJ Publishing Group Limited (BMJ) and may not have been peer-reviewed. Any opinions or recommendations discussed are solely those of the author(s) and are not endorsed by BMJ. BMJ disclaims all liability and responsibility arising from any reliance placed on the content. Where the content includes any translated material, BMJ does not warrant the accuracy and reliability of the translations (including but not limited to local regulations, clinical guidelines, terminology, drug names and drug dosages), and is not responsible for any error and/or omissions arising from translation and adaptation or otherwise.

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