Define the question, judge the methods and results with fair criteria, then write a concise take that helps real care decisions.
Medical review cheatsheet by study type
| Study Type | Core Questions To Ask | Fast Red Flags |
|---|---|---|
| Randomized Trial | Was allocation concealed? Was follow-up complete? Was analysis by intention-to-treat? Were outcomes patient-centered? | Large loss to follow-up; unequal co-interventions; per-protocol only; selective outcomes |
| Cohort / Case-Control | Were groups comparable at baseline? Were exposures and outcomes measured the same way? Were confounders adjusted well? | Immortal time bias; unclear exposure window; unmeasured confounding; post-hoc subgroup cherry-picking |
| Cross-Sectional | Is the sample representative? Are measures valid and reliable? Is temporality acknowledged? | Convenience sample; weak instruments; causal claims from one time point |
| Diagnostic Accuracy | Was the reference standard appropriate? Was blinding used? Was the spectrum of disease broad? | Case-control design; verification bias; partial blinding; threshold selected post-hoc |
| Systematic Review / Meta-analysis | Was the question crisp? Search complete? Risk of bias assessed? Heterogeneity handled well? | Opaque search; mixing apples and oranges; unregistered protocol; small-study effects ignored |
| Qualitative | Was sampling purposeful and clear? Was coding transparent? Did quotes back themes? | Token quotes; vague methods; single coder with no checks; thin context |
Steps for doing a journal article review in medicine (clinic routine)
Pick the right paper
Match the topic to a real patient, a teaching goal, or a policy question. Scan the title, abstract, and journal scope. Favor studies with clear clinical outcomes, a sample that looks like your setting, and transparent methods.
Frame the clinical question with PICO
Write one line: Population, Intervention, Comparison, Outcome. That line guides every judgment you make later. If the paper’s PICO differs from yours, say so early, since that gap drives applicability.
Scan first, then read deep
Do a fast pass: figures, tables, outcomes, and methods headings. Mark what you’ll verify on a careful pass: randomization or recruitment, exposure and outcome definitions, follow-up, missing data, and how they handled confounding. Keep the abstract at arm’s length until the end.
Judge methods without jargon
For trials, look for sequence generation, allocation concealment, blinding where possible, and intention-to-treat. For observational work, look for a prespecified cohort or case-control scheme, clear exposure timing, and stable measurement across groups. For reviews, look for a protocol, a full search, duplicate screening, and a plain risk-of-bias summary.
Run the numbers you need
Extract the event counts or means, then compute absolute risk change, number needed to treat or harm, and confidence intervals. When a CI crosses the null (e.g., risk ratio near 1.0 or mean difference near 0), the study is compatible with no clear effect at the chosen level. For diagnostic work, write down sensitivity, specificity, and likelihood ratios; then apply them to a pre-test probability that fits your clinic to get a post-test estimate.
Check bias and harms
List likely biases: selection, performance, detection, attrition, and reporting. Note any industry funding and how outcomes align with sponsor interests. Scan adverse events with the same care you give benefits, since side effects and burdens matter to patients as much as wins.
See if it fits your patient
Age, comorbidity, baseline risk, access, and values change what a result means. A small relative effect can mean a big absolute change in a high-risk clinic and a tiny change in a low-risk group. Spell out what you would tell one patient who matches the study and one who does not.
Write the review
Use short sections: one-line take, study basics, what they did, what they found, what it means, strengths, limits, and how you’d apply it. Keep claims tied to the data. If a claim rests on a subgroup, check if the subgroup was prespecified and if an interaction test backs it.
Share and archive
Post your summary to your team drive folder. Add the full citation, a link, your one-line take, and tags for topic and study type. That way you can reuse it for teaching, guidelines work, or quality rounds later.
Guide on how to review a medical journal article: reporting and ethics
Transparent reporting helps you judge a study fast. For trials, CONSORT checklists set the fields that make a paper clear. For systematic reviews, PRISMA lists the items that make a review usable. For observational research, STROBE lists items for cohort, case-control, and cross-sectional work. For diagnostic accuracy, STARD sets the fields you should see. When you write your own review notes for a journal club or a committee, mirror these checklists so readers can scan your work.
Conflicts of interest and author roles also shape trust. The ICMJE Recommendations spell out disclosure rules, data sharing, trial registration, and the responsibilities tied to peer review. When a paper discloses funding or prior registrations, cite them in your summary so readers can check.
Numbers you’ll use in a medical article review
| Metric | How To Read It | Quick Calc Or Rule |
|---|---|---|
| Absolute Risk Change (ARC) | Difference in event rates between groups; the number that speaks to patients | ARC = Control risk − Treatment risk; NNT = 1/|ARC| |
| Risk Ratio / Odds Ratio | Relative effect; best paired with ARC | CI excluding 1.0 signals a clear direction at the stated level |
| Hazard Ratio | Time-to-event effect; assumes proportional hazards | Plot Kaplan–Meier curves; check parallel log-log lines if shown |
| Mean Difference | Difference in averages on a continuous scale | Standardize if scales vary; check MCID to judge real-world value |
| Likelihood Ratios | Link test results to pre- and post-test odds for diagnosis | Use a Fagan nomogram or mental math: LR+ >10 or LR− <0.1 shifts are strong |
| I2 In Meta-analysis | Rough gauge of between-study scatter | Plan random-effects when heterogeneity is wide; probe sources |
Checklists by design
Randomized trials
Look for concealed allocation, balance at baseline, blinding where practical, adherence tracking, and full follow-up. Ask if outcomes match patient priorities and whether harms were tracked with the same energy as benefits. Per-protocol and as-treated runs can mislead when drop-outs differ by arm, so keep intention-to-treat as your anchor.
Observational studies
Confirm that exposure comes before outcome and that measurement is the same across groups. Review how the team dealt with confounding: restriction, matching, stratification, regression, or propensity scores. Check for over-adjustment that blocks the causal path you care about, and for sensitivity checks that show the main take is not fragile.
Diagnostic accuracy studies
Make sure participants span the range you see in clinic, not just clear cases and clear non-cases. The reference standard should be appropriate and applied regardless of the index test result. Pay attention to thresholds, handling of indeterminate results, and whether readers of the index and reference tests were blinded to each other.
Systematic reviews and meta-analyses
Start with the question and protocol. A good review reports a full search across databases and gray sources, dual screening, a risk-of-bias table, and a plan for synthesis. In the forest plot, pair relative effects with absolute numbers at a baseline risk that matches your clinic. If trials differ in populations or doses, look for subgroup and sensitivity runs that test the story.
Qualitative research
Read the sampling plan, the interview or observation guide, and how codes and themes were built. Multiple coders or member checks can raise trust. The best papers link quotes to themes and explain how the team reached saturation. Use these studies to shape questions, outcomes, and patient-centered decisions.
Stat checks without a stats degree
Stick to a small set of tools and you’ll stay on track. Confidence intervals show the range of effect sizes that fit the data. If the range includes the null, say the study cannot rule out no effect. If the range is narrow and far from the null, you can speak with more confidence about size and direction. P-values tell you how surprising the results would be if there were no true effect; they do not tell you the chance a treatment works. When many tests are run, false positives creep in, so give more weight to prespecified outcomes and a clear primary endpoint.
For subgroup results, ask three things: was the subgroup prespecified, is the pattern large and plausible, and did the authors test for interaction? A bar chart with wide swings looks tempting, but an interaction test keeps you honest. For non-inferiority trials, pay attention to the margin: it should reflect a trade-off patients would accept, such as fewer clinic visits or lower cost.
Write review notes that others can use
Use a fixed scaffold so readers can skim fast. Here’s a simple layout that works for teaching, rounds, and committee packets.
One-line take
State the intervention, the setting, the main result with a number, and who it helps or doesn’t.
Study basics
Design, place, dates, sample size, who was in and out, follow-up, and primary outcome.
What they did
Randomization or recruitment, exposure and comparator, dose or threshold, and how outcomes were measured and timed.
What they found
Primary result with ARC and NNT/NNH when possible; major secondary outcomes; harms and burdens.
Strengths
Method choices that reduce bias, good outcome selection, clean reporting, and patient-centered measures.
Limits
Bias risks you saw, generalizability gaps, missing data issues, short follow-up, or model fragility.
What it means for care
Who would you offer this to on Monday? Who would you not? What shared decision script fits the numbers?
Common pitfalls and fast fixes
- Chasing p-values: Lead with effect sizes and CIs. Then mention p-values.
- Relative effects without base rates: Always pair risk ratios with ARC.
- Outcome switching: Check registries and protocols when linked in the paper.
- Misreading survival curves: Check absolute differences at time points that matter to patients.
- Too much trust in models: See if results hold with different model choices and if assumptions are shown.
- Throwing out non-conclusive results: A wide CI means “we don’t know yet,” not “no effect.”
- Publication bias in reviews: Seek funnel plots or small-study checks; look for trial registries to see what never got published.
Template: fifteen-minute review flow
- Write your PICO.
- Skim figures and tables; jot outcomes and time points.
- Read methods with a bias checklist for the study type you’re holding.
- Pull the numbers you need and compute ARC and NNT/NNH.
- Scan harms and burdens.
- Judge fit for your clinic: baseline risk, setting, access, values.
- Draft your one-line take and the short sections above.
- Link to reporting and appraisal aids such as BMJ’s “How to read a paper” series and the Cochrane Handbook.
Quick tips for teaching and committees
Assign roles: one person runs the methods check, one runs the numbers, one judges fit for patients, one writes the summary. Timebox each part. Close with a clear vote on “ready for clinic,” “needs more data,” or “save for later.” Save every review in a shared folder with tags so your group builds a living library.
From notes to action in clinic
Turn a paper into care steps with a script. Open with the choice the patient faces, then share the absolute numbers in plain speech: “Out of 100 people like you on this drug, about 8 fewer had the event over one year, while 2 more had side effects that needed a visit.” Offer options that fit values, costs, and access. If the answer is “not ready,” set a plan to revisit when new trials land or when your patient’s risk changes.
For teams, create a one-page brief per topic with your one-line take, the best figure copied with permission, and a paragraph on how the team will act. Tie that to order sets, checklists, or patient handouts. Small, steady updates beat large rewrites. As your library grows, you’ll spend less time hunting and more time caring.