How reporting guidelines help researchers and the scientific community

Researchers, editors, and reviewers discuss how they use reporting guidelines.
Published

April 1, 2025

John Doe – Early Career Researcher

As someone who’s new to academic writing, I honestly didn’t know where to start—I didn’t have a writing process, and staring at the blank page felt overwhelming. I wasn’t sure what I was supposed to include or how best to structure it all. My PhD supervisor suggested I use a reporting guideline, and they were a total game-changer. They gave me something concrete to work with—like a to-do list that helped me get past that initial block and start putting information down on the page.

The first time I used one, I found it a bit tricky. It felt like a lot to take in, and although it helped me jot down information, my manuscript felt really long and a bit disjointed. But then I realised I didn’t have to follow it rigidly like a script. It was just a starting point. Once I had the key information written out, I could decide how best to organise it to make the paper flow well. Some things worked better in a table, some in the main text, and I moved a few details into the supplementary materials. That flexibility made it feel much more manageable.

I was also a bit nervous about being too transparent—especially when it came to discussing limitations. But I’ve come to realise that journals and reviewers (and my supervisor and viva assessors!) really value honesty and thoughtful reflection. It shows that you understand your work deeply and are engaging with it critically. Overall, using reporting guidelines has made me feel much more confident and in control of the writing process.

Jane Doe – Experienced Researcher

I first came across reporting guidelines when a journal asked me to complete a checklist at the point of submission. At the time, I found it a bit annoying—after spending weeks refining and polishing the manuscript, the idea of going back to adjust the structure or add missing details felt like frustrating red tape. I completed the checklist, of course, but it felt like a box-ticking exercise rather than something helpful.

The next time I wrote a paper, I decided to use the relevant reporting guideline right from the start—just to see if it made the process smoother. It turned out to be a really practical tool. Having a clear list of what information to include made it easier to draft the paper, and I found myself making fewer revisions later on. Over time, using guidelines has become second nature. I now refer to them not just when writing papers, but also when planning new studies, preparing funding applications, and writing protocols. By the time I get to the journal submission stage, completing the checklist is quick and reassuring—I already know I’ve covered what’s important.

I also use reporting guidelines when teaching my students how to write. For researchers who are still developing their writing habits, these guidelines provide structure and clarity. They help demystify what’s expected and build a strong foundation for clear, transparent research communication. If you’re just starting out, I’d really encourage you to give them a try—not just at the end of your writing, but as a tool to guide you from the beginning. It’s made a big difference to my work

Julius Doe – Editor of prestigious journal

As a senior managing editor of a medical journal, I care deeply about the quality and usability of the research we publish—and that starts with good reporting. I’ve instructed my editorial and peer review teams to check all submissions against the appropriate reporting guidelines, whether that’s CONSORT, STROBE, PRISMA, or another guideline. They use these guidelines to assess whether key information is present, clear, and transparent—such as how the study was conducted, who was involved, what interventions were used, and how outcomes were measured. We don’t expect every study to be perfect—research is complex, and limitations are inevitable—but we do expect authors to report their work fully and honestly. A clearly reported study, with a thoughtful discussion of its limitations, is far more valuable to the scientific community than one that hides its flaws. Reporting guidelines help ensure that published research can be understood, used, built upon, and cited—and that’s essential for real-world impact, and essential to maintaining our reputation as a leading journal.

Janet Doe – Possible second editor or technical editor

I work in the editorial office of a well-respected medical publisher, where my team supports dozens of journals and handles over 100 new submissions every week. Our role is to ensure that each manuscript meets a set of minimal standards before it’s passed on to the academic editor for peer review. These initial checks include confirming that ethical declarations are in place, the manuscript is properly structured, and—crucially—that it adheres to the appropriate reporting guidelines.

When a reporting guideline is required, we expect authors to submit a completed checklist alongside their manuscript. To help maintain quality and consistency, our team typically selects a few items at random from the checklist and cross-checks them against the manuscript to ensure the information is actually there. If a checklist is missing or key information hasn’t been reported, we contact the authors and hold the submission until the issues are resolved.

These requirements aren’t meant to be obstacles—they’re in place to help ensure your research is clear, complete, and ready for peer review. Using reporting guidelines from the start will not only improve the quality of your manuscript but also help it move through the editorial process more smoothly.

June Doe – Peer reviewer and evidence synthesiser

When I peer review manuscripts for journals, I always refer to reporting guidelines to check whether the study has been reported clearly and completely. These guidelines help me spot missing details—things like how the sample was selected, exactly what interventions were used, or how outcomes were measured. I also recommend reporting guidelines to junior colleagues who are new to peer reviewing—they’re a great way to structure your review and make sure you’re thorough. Although I always remind my colleagues that checking for adherence to a reporting guideline is only the first step of a good review. Information has to be present before you can consider the strengths and weaknesses of research.

I ask authors to fill in those gaps, not just so I can properly assess the quality and validity of the research, but because I know those details matter to other people down the line. Even if something doesn’t seem essential for the initial peer review, including it helps ensure your work is usable, reproducible, and ultimately more impactful.

For example, I do a lot of systematic reviews and meta-analyses of health research. To do my job effectively, I need to be able to find relevant studies, understand exactly what was done, extract key information, and compare results across multiple papers. When articles are poorly reported — e.g., the intervention isn’t clearly defined, the patient group isn’t described, or key terms are missing from the title and abstract — it can be incredibly difficult to even locate them through database searches. If important details are vague or missing, I may have to exclude the study altogether because I can’t be confident about what was done or how it fits with the other evidence. That not only weakens the quality of the review, but also means the original study misses out on being cited or informing future research and policy. The research articles that are most useful to me are the ones where all the key information is clearly reported, limitations are acknowledged, and reporting guidelines have been followed. These are the studies I can include, cite, and build on—so they ultimately have more influence and impact.

Reporting Guidelines are recommendations to help describe your work clearly

Your research will be used by people from different disciplines and backgrounds for decades to come. Reporting guidelines list the information you should describe so that everyone can understand, replicate, and synthesise your work.

Reporting guidelines do not prescribe how research should be designed or conducted. Rather, they help authors transparently describe what they did, why they did it, and what they found.

Reporting guidelines make writing research easier, and transparent research leads to better patient outcomes.

Easier writing

Following guidance makes writing easier and quicker.

Smoother publishing

Many journals require completed reporting checklists at submission.

Maximum impact

From nobel prizes to null results, articles have more impact when everyone can use them.

Who reads research?

You work will be read by different people, for different reasons, around the world, and for decades to come. Reporting guidelines help you consider all of your potential audiences. For example, your research may be read by researchers from different fields, by clinicians, patients, evidence synthesisers, peer reviewers, or editors. Your readers will need information to understand, to replicate, apply, appraise, synthesise, and use your work.

Cohort studies

A cohort study is an observational study in which a group of people with a particular exposure (e.g. a putative risk factor or protective factor) and a group of people without this exposure are followed over time. The outcomes of the people in the exposed group are compared to the outcomes of the people in the unexposed group to see if the exposure is associated with particular outcomes (e.g. getting cancer or length of life).

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Case-control studies

A case-control study is a research method used in healthcare to investigate potential risk factors for a specific disease. It involves comparing individuals who have been diagnosed with the disease (cases) to those who have not (controls). By analysing the differences between the two groups, researchers can identify factors that may contribute to the development of the disease.

An example would be when researchers conducted a case-control study examining whether exposure to diesel exhaust particles increases the risk of respiratory disease in underground miners. Cases included miners diagnosed with respiratory disease, while controls were miners without respiratory disease. Participants' past occupational exposures to diesel exhaust particles were evaluated to compare exposure rates between cases and controls.

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Cross-sectional studies

A cross-sectional study (also sometimes called a "cross-sectional survey") serves as an observational tool, where researchers capture data from a cohort of participants at a singular point. This approach provides a 'snapshot'— a brief glimpse into the characteristics or outcomes prevalent within a designated population at that precise point in time. The primary aim here is not to track changes or developments over an extended period but to assess and quantify the current situation regarding specific variables or conditions. Such a methodology is instrumental in identifying patterns or correlations among various factors within the population, providing a basis for further, more detailed investigation.

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Systematic reviews

A systematic review is a comprehensive approach designed to identify, evaluate, and synthesise all available evidence relevant to a specific research question. In essence, it collects all possible studies related to a given topic and design, and reviews and analyses their results.

The process involves a highly sensitive search strategy to ensure that as much pertinent information as possible is gathered. Once collected, this evidence is often critically appraised to assess its quality and relevance, ensuring that conclusions drawn are based on robust data. Systematic reviews often involve defining inclusion and exclusion criteria, which help to focus the analysis on the most relevant studies, ultimately synthesising the findings into a coherent narrative or statistical synthesis. Some systematic reviews will include a meta-analysis.

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Systematic review protocols

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Meta analyses of Observational Studies

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Randomised Trials

A randomised controlled trial (RCT) is a trial in which participants are randomly assigned to one of two or more groups: the experimental group or groups receive the intervention or interventions being tested; the comparison group (control group) receive usual care or no treatment or a placebo. The groups are then followed up to see if there are any differences between the results. This helps in assessing the effectiveness of the intervention.

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Randomised Trial Protocols

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Qualitative research

Research that aims to gather and analyse non-numerical (descriptive) data in order to gain an understanding of individuals' social reality, including understanding their attitudes, beliefs, and motivation. This type of research typically involves in-depth interviews, focus groups, or field observations in order to collect data that is rich in detail and context. Qualitative research is often used to explore complex phenomena or to gain insight into people's experiences and perspectives on a particular topic. It is particularly useful when researchers want to understand the meaning that people attach to their experiences or when they want to uncover the underlying reasons for people's behavior. Qualitative methods include ethnography, grounded theory, discourse analysis, and interpretative phenomenological analysis.

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Case Reports

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Diagnostic Test Accuracy Studies

Diagnostic accuracy studies focus on estimating the ability of the test(s) to correctly identify subjects with a predefined target condition, or the condition of interest (sensitivity) as well as to clearly identify those without the condition (specificity).

Prediction Models

Prediction model research is used to test the accurarcy of a model or test in estimating an outcome value or risk. Most models estimate the probability of the presence of a particular health condition (diagnostic) or whether a particular outcome will occur in the future (prognostic). Prediction models are used to support clinical decision making, such as whether to refer patients for further testing, monitor disease deterioration or treatment effects, or initiate treatment or lifestyle changes. Examples of well known prediction models include EuroSCORE II for cardiac surgery, the Gail model for breast cancer, the Framingham risk score for cardiovascular disease, IMPACT for traumatic brain injury, and FRAX for osteoporotic and hip fractures.

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Animal Research

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Quality Improvement in Healthcare

Quality improvement research is about finding out how to improve and make changes in the most effective way. It is about systematically and rigourously exploring "what works" to improve quality in healthcare and the best ways to measure and disseminate this to ensure positive change. Most quality improvement effectiveness research is conducted in hospital settings, is focused on multiple quality improvement interventions, and uses process measures as outcomes. There is a great deal of variation in the research designs used to examine quality improvement effectiveness.

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Economic Evaluations in Healthcare

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Meta Analyses

A meta-analysis is a statistical technique that amalgamates data from multiple studies to yield a single estimate of the effect size. This approach enhances precision and offers a more comprehensive understanding by integrating quantitative findings. Central to a meta-analysis is the evaluation of heterogeneity, which examines variations in study outcomes to ensure that differences in populations, interventions, or methodologies do not skew results. Techniques such as meta-regression or subgroup analysis are frequently employed to explore how various factors might influence the outcomes. This method is particularly effective when aiming to quantify the effect size, odds ratio, or risk ratio, providing a clearer numerical estimate that can significantly inform clinical or policy decisions.

How Meta-analyses and Systematic Reviews Work Together

Systematic reviews and meta-analyses function together, each complementing the other to provide a more robust understanding of research evidence. A systematic review meticulously gathers and evaluates all pertinent studies, establishing a solid foundation of qualitative and quantitative data. Within this framework, if the collected data exhibit sufficient homogeneity, a meta-analysis can be performed. This statistical synthesis allows for the integration of quantitative results from individual studies, producing a unified estimate of effect size. Techniques such as meta-regression or subgroup analysis may further refine these findings, elucidating how different variables impact the overall outcome. By combining these methodologies, researchers can achieve both a comprehensive narrative synthesis and a precise quantitative measure, enhancing the reliability and applicability of their conclusions. This integrated approach ensures that the findings are not only well-rounded but also statistically robust, providing greater confidence in the evidence base.

Why Don't All Systematic Reviews Use a Meta-Analysis?

Systematic reviews do not always have meta-analyses, due to variations in the data. For a meta-analysis to be viable, the data from different studies must be sufficiently similar, or homogeneous, in terms of design, population, and interventions. When the data shows significant heterogeneity, meaning there are considerable differences among the studies, combining them could lead to skewed or misleading conclusions. Furthermore, the quality of the included studies is critical; if the studies are of low methodological quality, merging their results could obscure true effects rather than explain them.

Protocol

A plan or set of steps that defines how something will be done. Before carrying out a research study, for example, the research protocol sets out what question is to be answered and how information will be collected and analysed.

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