A medical literature review example shows a clear question, a transparent search, screened studies, bias checks, and a concise synthesis.
Need a model you can reuse without fluff? This page lays out a practical, journal-friendly layout that matches common expectations in medicine. You’ll see what goes where, how to craft a tight search, and the exact fields to extract so your write-up lands cleanly on the first read.
What A Medical Literature Review Includes
A medical review sums up what the best available studies say on a focused question. It should be transparent from start to finish: question, search, screening, extraction, synthesis, and limits. The sections below reflect widely used reporting habits and help a reader scan fast.
| Section | What To Include | Practical Tips |
|---|---|---|
| Title & Abstract | Topic, design, setting, key outcomes, high-level takeaways. | Keep the abstract structured; match the journal’s headings. |
| Question | PICOT or a near match; define population, exposure or intervention, comparator, outcomes, and time. | Write one line first, then expand to a paragraph. |
| Eligibility Rules | Study types, years, languages, settings, and outcome domains. | State reasons to exclude before you start screening. |
| Search Strategy | Databases, dates, search strings, and any handsearching. | Log every date and exact query text. |
| Screening | Number screened, number included, and reasons for exclusion. | Track dual screening and how conflicts were settled. |
| Data Extraction | Fields, tools, and who extracted; handle duplicates and missing data. | Pilot the form on five papers and revise once. |
| Bias Assessment | Tool chosen and how ratings were reached. | Describe training and calibration in a line or two. |
| Synthesis | Narrative or meta-analysis; effect measures and models. | Explain why you pooled or why you didn’t. |
| Certainty | Approach used to rate certainty across outcomes. | Summarize by domain and end with an overall level. |
| Findings | What the studies show, where they agree, where they don’t. | Lead with the outcomes your audience cares about most. |
| Limits | Scope limits, data gaps, and threats to validity. | Stick to evidence-linked limits only. |
| Implications | What the field can do today, what needs study next. | Keep it action-oriented and brief. |
To align your report with common checklists, cite the PRISMA 2020 guidance for transparent reporting, lean on the Cochrane Handbook for core methods, and shape database work with the PubMed filters help page.
Medical Literature Review Example: Ready-To-Copy Layout
Use the scaffold below as a starting point. Replace the bracketed parts with your topic, then tighten the prose. Keep the tone clear and neutral.
Title
[Topic]: A Review Of Evidence From [Study Types, Years].
Abstract
Background: State the gap the review fills and the target audience. Methods: Note databases, dates, screening, and bias tools. Results: Give counts and the top outcome signals. Conclusion: Give the plain-language bottom line.
Question
In [Population], does [Intervention or Exposure], compared with [Comparator], change [Outcomes] over [Time or Setting]?
Eligibility
Include [study types] addressing the question. Exclude case reports, conference abstracts without full text, and non-peer-reviewed items. Include English and non-English where possible and list any limits used.
Information Sources
Database list with coverage dates, plus trial registries and reference lists of key reviews. If a librarian helped, credit them here.
Search Strategy
Show at least one full strategy. Note MeSH, keywords, and Boolean logic. Record the last search date.
(("Hypertension"[MeSH] OR high blood pressure[tiab])
AND ("Home Blood Pressure Monitoring"[MeSH] OR home monitor*[tiab])
AND (adult*[tiab] OR "Adult"[MeSH])) NOT (animal*[tiab])
Study Selection
Two reviewers screened titles and abstracts, then full texts. Disagreements were settled by discussion or a third reviewer. Counts map to a flow diagram.
Data Extraction
One reviewer extracted, and a second checked. Fields included study design, setting, sample size, eligibility, intervention details, comparators, follow-up, outcomes, effect estimates, and notes.
Bias Assessment
Randomized trials used RoB 2; cohort and case-control studies used a suitable tool. Ratings were made at the outcome level where needed.
Synthesis Plan
When studies were sufficiently alike in design and outcome metrics, we pooled effects with random effects models. Otherwise, we used structured narrative synthesis with tables.
Certainty Rating
We judged certainty across outcomes with GRADE and summarized domains before giving an overall level.
Findings
Summarize the direction and size of effects across key outcomes first. Include context such as adherence, safety, or feasibility if reported.
Limits
Note design limits, small samples, inconsistent measures, or short follow-up. Tie each limit to the influence on confidence or use.
Implications
Spell out near-term practice moves and the most pressing study needs. Keep claims tight and evidence-linked.
Searching The Medical Literature: From PICOT To PRISMA
Start with a precise PICOT line. Convert that to controlled terms and plain text terms. Combine with Boolean logic and test recall on a set of known studies. When it works, lock the final string and document the date for each database.
Databases And Terms
- PubMed: Blend MeSH with free text and field tags; learn how mapping works through the MeSH tutorials.
- Embase: Pair Emtree terms with text words and apply study filters if needed.
- CINAHL or PsycINFO: Add allied health or mental health coverage for broader topics.
Build And Test
Write variants for each concept. Truncate where sensible. Add proximity operators if your database supports them. Run pilots, scan the first hundred hits, and tune. Save every strategy and export the full set for screening software.
Screening Workflow
De-duplicate, run a title-abstract pass, then full texts. Keep reason codes tight: wrong population, wrong design, wrong outcome, no comparator, duplicate, or not in scope. Record counts for each code.
Data Fields That Matter
Pick fields that let a reader judge transferability and bias risk. Use a table shell in your form so figures land in the right place later.
Example Of A Medical Literature Review: Step-By-Step Build
Here’s a compact build you can tailor. Topic: home blood pressure monitoring to support clinic visits in adults with hypertension.
Question Line
In adults with diagnosed hypertension, does structured home monitoring, compared with usual care alone, improve systolic and diastolic control over six to twelve months?
Eligibility Rules
Include randomized and prospective cohort studies in primary care or community settings that report blood pressure at follow-up. Exclude pediatric samples, gestational hypertension, and device validation studies.
Search Snapshot
Databases: PubMed, Embase, and CINAHL. From database start to March 2025. Full strings logged and archived. Grey sources: trial registries and device safety alerts.
Screening Outcome
Out of two thousand one hundred forty-five records, one hundred twelve full texts were reviewed, and twenty-four studies met criteria. Main exclusions were wrong population and lack of a comparator.
Extraction Shell
Key fields: device type, monitoring schedule, co-interventions such as telemonitoring or pharmacist input, adherence targets, outcome timing, and adverse events.
Bias Checks
Trials were rated with RoB 2. Two non-randomized studies used a validated tool suited to cohort designs. Calibration was done on three papers before full rating.
Synthesis Notes
Where outcomes and time points aligned, we pooled mean differences for systolic and diastolic pressure. Heterogeneity was explored by device plus support model.
Plain-Language Findings
Across trials, structured home monitoring paired with light support tended to reduce systolic pressure by a small margin versus usual care at six to twelve months. Safety signals were rare. Gains faded when support ended.
Certainty Snapshot
Certainty for the systolic outcome landed at moderate due to inconsistency in support models. Diastolic effects were less consistent.
Synthesis And Reporting: Narrative Or Meta-Analysis
Narrative suits mixed designs, varied measures, or sparse data. Meta-analysis suits well-aligned designs and outcomes. State effect measures in plain terms, explain the model, and show a table of study features so readers see why pooling was fair.
| Study Type | Bias Tool | When It Fits |
|---|---|---|
| Randomized Trial | RoB 2 | Outcome-level judgment with domains for randomization, deviations, missing data, measurement, and reporting. |
| Cohort / Case-Control | Validated tool suited to non-randomized studies | Check confounding, selection, classification, deviations, missing data, measurement, and reporting. |
| Cross-Sectional | Design-specific checklist | Use sparingly for intervention effects; better for prevalence and associations. |
Certainty Across Outcomes
Use a transparent approach such as GRADE. Start at high for trials and lower for concerns like risk of bias, inconsistency, indirectness, imprecision, or publication bias. End with a single level per outcome and say why.
Write-Up Checklist And Formatting
Short sentences and clear headings help scanning. Lead sections with the most useful points. Keep numbers consistent across text, tables, and any flow diagrams. Use active voice where possible. Keep tables narrow and easy to parse.
House Style To Keep Readers Moving
- Use parallel structure in headings.
- Place key numbers early in each paragraph.
- Label acronyms at first use and keep a short list.
- Prefer standard effect units and list units next to each number.
- Place tables near the first mention.
Common Pitfalls To Avoid
Scope creep, vague outcomes, and thin search notes make reviews hard to trust. Don’t screen without predefined reasons to exclude. Don’t skip dual checks where stakes are high. Don’t hide a null result deep in the text. If you can’t pool, say so and explain why in one line.
Short Annotated Medical Literature Review Example
Background: Home monitoring is widely available and often bundled with light coaching. Methods: We searched three databases through March 2025, screened in pairs, extracted in pairs, rated trials with RoB 2, and summarized certainty with GRADE. Results: Twenty-four studies met rules. Trials showed small systolic gains at six to twelve months when support was active; gains waned after support ended. Limits: Support models varied, and outcome timing differed. Implications: Teams can pair home checks with brief touchpoints, then plan maintenance to keep gains.
When you cite guidance or methods, link to the sources readers know: the PRISMA statement paper, the Cochrane methods book, and the GRADE working group.