A medical literature review is written by setting a tight question, running a structured search, appraising studies, and blending results clearly.
Readers come here to learn how to write a medical literature review that reads and stands up. The path isn’t mystery: set the question, design the search, screen, chart data, judge bias, synthesize, and write with plain headings and honest limits.
Foundation: Purpose, Scope, And Review Type
State the aim and the review type. The label drives methods and reader expectations. Common routes include narrative, scoping, and systematic reviews with or without meta-analysis. Pick the route that fits the aim and timeline. State PICO or PEO, setting, and time frame.
| Decision Area | What You Define | Why It Matters |
|---|---|---|
| Question Format | PICO/PEO/PECOS framing | Keeps eligibility tight and search terms aligned |
| Review Type | Narrative, scoping, systematic, rapid | Determines depth, screening rules, and outputs |
| Study Designs | Trials, cohorts, case-control, cross-sectional | Guides inclusion and later appraisal tools |
| Outcomes | Primary and secondary | Directs data items and synthesis plan |
| Time Window | Years searched; updates | Signals freshness and reduces bias |
| Language/Setting | Languages, regions | Shapes feasibility and reach |
| Protocol | Registration or plan | Locks methods before you start |
How A Medical Literature Review Should Be Written: Step-By-Step
1) Draft A Protocol
Write a short plan that lists the question, eligibility rules, databases, screening steps, data items, bias tools, and synthesis approach. Keep it short but precise. If the review is systematic, register or post the plan. Even for a narrative piece, a one-page plan prevents drift and shows care.
2) Build The Search
Map concepts to subject headings and text words. Combine with AND/OR, limit dates or languages only when justified, and record every string. Use at least two databases and add trial registries when needed. Save strategies and export full fields for de-duplication.
3) Screen In Two Stages
First, scan titles and abstracts against clear criteria. Next, read full texts for a final call. Use two independent reviewers when you can, with a tie-breaker rule. Keep a log of exclusions with brief reasons. This audit trail feeds your flow diagram and keeps the story clean.
4) Extract Data With A Form
Create a form before you start. Capture study design, setting, participant counts, interventions or exposures, comparators, outcomes, effect measures, follow-up, and funding. Pilot the form on a few studies, then lock it. Train anyone who’s helping so the fields stay consistent.
5) Judge Risk Of Bias
Pick tools that match design. Use trial tools for randomized studies and design-specific tools for observational work. Rate each domain with notes that cite page lines. Keep judgments separate from results at first; you’ll link them during synthesis.
6) Plan The Synthesis
Decide whether a meta-analysis is feasible. If not, group studies by design, dose, setting, or outcome window and synthesize narratively with structured subheads and tables. When pooling, predefine effect measures, models, and handling of heterogeneity and small-study effects. Set rules for subgroup checks and sensitivity runs.
7) Write With A Reader’s Map
Use clear sectioning: introduction, methods, results, and a short implications section. Keep claims tight to the data. Report the flow from records found to studies included. Present study and outcome tables before any takeaways. Close with limits, measured notes linked to certainty, and what would change the picture.
Method Details That Editors Look For
Search Transparency
Print full search strings for each source in an appendix. List the platforms (e.g., PubMed, Embase), the run dates, and any filters. State how you de-duplicated records. Note if you added hand-searching, citation chasing, or author contact.
Screening And Selection
State who screened, how disagreements were handled, and your software. Include a flow diagram that shows records identified, screened, excluded, and included, with reasons at the full-text stage.
Data Items And Outcomes
Define every data item ahead of time. Pick primary outcomes that match the question and patient-visible endpoints when possible. Explain any computed fields, such as converting medians to means or extracting hazard ratios from plots.
Bias Appraisal
Report tool names and version. Give domain-level judgments with short quotes or line cites. Keep summary figures legible on mobile. Tie the risk profile to how you weigh each finding in the synthesis.
Certainty Of Evidence
Summarize the body of evidence by outcome. Rate certainty with transparent reasons for any downgrades or upgrades. Align language in the abstract and body to that certainty so readers don’t overread a weak base.
Two widely used anchors can help you check methods and reporting detail: the PRISMA 2020 checklist for reporting and the Cochrane Handbook methods for process and synthesis choices.
Writing Style That Builds Trust
Clarity beats jargon. Plain beats puff. Use short sentences and plain words for methods and results. Define terms once, then keep them consistent. Report numbers with denominators and time windows. Mark estimates with confidence intervals, not only p-values. Avoid claims that go beyond the design or the data.
Visuals And Tables
Figures should earn their spot. A flow diagram shows study selection. A forest plot displays pooled effects. Summary tables give readers a fast map. Keep labels readable, note scale choices, and avoid chart junk that steals attention.
When Meta-Analysis Fits
Pool only when populations, exposures or interventions, and outcomes align. Predefine fixed or random effects and how you’ll handle variance. Report heterogeneity, small-study signals, and planned subgroup runs. If pooling doesn’t fit, say so and show a careful narrative route.
Common Mistakes To Avoid
- Vague questions that sprawl into aimless searches.
- Single-database searches that miss half the field.
- Eligibility rules that shift mid-stream without a trace.
- No record of excluded full texts or missing reasons.
- Mixing outcomes with different time frames in one pool.
- Hiding bias judgments in prose with no tables or figures.
- Conflating low certainty with strong action verbs.
Results Section: What To Show
Open with the flow of studies. Then describe the included studies and their settings. Present primary outcomes first, then secondary ones. If you pool data, lead with the main effect, then range and caveats. If not, group by concept with consistent subheads, and point to tables that carry the load.
| Section | Must Show | Checks |
|---|---|---|
| Flow | Records found, screened, excluded, included | Counts add up across stages |
| Study Summary | Designs, sizes, settings | Table with footnotes for quirks |
| Outcomes | Effect sizes with intervals | Units and time windows clear |
| Bias | Domain ratings per study | Figures plus notes |
| Certainty | Outcome-level ratings | Matches claims in text |
Discussion Section: How To Frame Claims
Start with the main answer to the question you set. Then set that answer next to study quality, size, and directness. Explain what the findings add to the field, where results line up or split, and why. State how bias may tilt effects high or low. Keep any practice notes measured and linked to certainty.
Limits And Strengths
State where methods could miss studies or misread effects. Give brief reasons, not blame. Note sample sizes, follow-up length, and any gaps in outcome reporting. Also note what you did well: duplicate screening, protocol use, clear data forms, and transparent tables.
Implications
Give readers something they can use: gaps for trials, endpoints that matter to patients, or ways to align later searches with registries. Keep tone steady and align verbs with certainty grades.
Submission Prep And Ethics
Disclose funding and any ties. Declare how you handled data from trials, including contact with authors. Share extraction forms and code if you can. Deposit the search strings and screening log. Pick a target journal whose scope fits the review and follow its guide for tables, figures, and word counts.
Workflow Tips For Busy Teams
Start early with a shared template so everyone writes into the same boxes and fields. Name files with dates and step tags; version control saves hours when edits flip back and forth. Keep a screening calendar and short stand-ups; small, steady blocks beat last-second sprints every time. Store decisions, not just PDFs; a one-line reason for each exclusion prevents repeats and debate. Write as you go: methods can be drafted while screening, and tables can be seeded during extraction.
Run a pilot meta-analysis on a subset to check feasibility before you commit to full pooling. Keep figures device-ready; test legends and axis labels on a phone before you call them done. Map roles to names for each step so tasks move even when someone is out or on leave. Batch author emails for missing data on one morning each week, then log replies in a simple sheet. So. Close loops with a short postmortem to capture fixes for next time too.
Mini Template You Can Reuse
Title And Abstract
Title states the question and review type. Abstract mirrors the main sections with a plain language lead. Add the registration number if it exists.
Methods
Question framing; sources and run dates; full strings; eligibility; screening process; data items; bias tools; synthesis plan; certainty approach.
Results
Flow counts; study and outcome tables; pooled effects with intervals or structured narrative groups; bias figures; certainty ratings.
Discussion
Answer, context, limits, and measured takeaways for practice or research.
Put It All Together
Good reviews read clean because the plan was clear, the search was traceable, the screening was steady, and the write-up matched the evidence. If you follow a protocol, show the flow, use fit-for-purpose bias tools, and link claims to certainty, you’ll give readers a review they can trust and reuse.