Systematic Review Techniques

Thinking of doing a Systematic Review? Read this first 👇 If you’re starting your research journey, you’ve probably heard that a systematic review is a “high-level” form of evidence synthesis. True, but it’s also one of the most misunderstood research designs among beginners. So, follow these guidelines: 1. Start with a crystal-clear question Use the PICO or SPIDER framework depending on your field. Your question isn’t just academic—it’s your GPS for the entire review. If your question is fuzzy, your review will wander. 2. Write and register your protocol Before you start searching, write your plan: What databases will you search? What inclusion/exclusion criteria? How will you assess quality? Then, register it on PROSPERO or OSF. (Why? It adds transparency and protects your effort from being duplicated.) 3. Search like a detective, not a tourist Don’t rely on just PubMed or Google Scholar! Use multiple databases (e.g., Scopus, Web of Science, Cochrane Library) and include grey literature (theses, reports, conference papers). Work with a librarian if possible — they’re gold for refining your strategy. 4. Screen with discipline Systematic = consistent. Use tools like Rayyan, Covidence, or EndNote to screen titles and abstracts. Always have two reviewers independently screen to reduce bias. 5. Assess quality, don’t just summarize Every study has strengths and flaws. Use tools like JBI, CASP, or Cochrane Risk of Bias checklists. This helps you weigh evidence, not just count it. 6. Synthesize with sense Quantitative? → Go for meta-analysis (if studies are similar). Qualitative? → Try thematic synthesis. Either way, tell a story — what do these studies collectively say? 7. Report transparently Follow PRISMA guidelines. Include your flow diagram, search strategy, and reasons for exclusions. It’s not just paperwork — it’s what makes your review trustworthy. PS: What’s one challenge you’ve faced while doing (or planning) a systematic review? Share in the comments. REPOST to help others.

How to Do a Meta-Analysis (Even Without a Research Mentor) No lab. No team. No problem. Here’s how I’m conducting meta-analyses on my own — and how you can, too. Step-by-step breakdown for beginners: 1. Choose a research question. It must be specific, focused, and clinically relevant. Example: Is there an association between root canal bacteria and breast cancer? 2. Register your protocol (optional but preferred). Use PROSPERO to register your research protocol. This builds credibility and prevents duplication. 3. Conduct a systematic literature search. Use PubMed, Embase, Scopus, Google Scholar. Build a strong Boolean search strategy. Example: ("root canal bacteria" OR "endodontic infection") AND ("breast cancer" OR "mammary carcinoma") 4. Screen and select studies. Use Rayyan.ai (free and easy) for blinded abstract and full-text screening. Apply inclusion and exclusion criteria based on your research focus. 5. Extract data. Use Excel or Google Sheets to extract sample sizes, outcomes, odds ratios, confidence intervals, etc. 6. Analyze the data. Use software like: RevMan (free from Cochrane) JASP (free and beginner-friendly) Stata, R, or Comprehensive Meta-Analysis (advanced) Calculate pooled effect sizes, heterogeneity (I²), and run sensitivity/subgroup analyses if needed. 7. Follow PRISMA guidelines. Your manuscript should include: PRISMA flow diagram Forest plot Risk of bias assessment (use tools like ROBINS-I or Cochrane RoB 2) Discussion and conclusion with clinical implications 8. Choose a journal and submit. Top journals like JAMA, BMJ Open, Cureus, or PLOS ONE accept systematic reviews and meta-analyses — yes, even from independent authors. Some are free; some charge article processing fees (APCs) — consider it an investment. Final Tips: Use Zotero or EndNote for referencing Use AI tools responsibly for grammar, PRISMA formatting, and visualizing plots Read and cite recent meta-analyses for structure and flow Moral of the story? You don’t need a supervisor or a research lab to publish. All you need is initiative, discipline, and the right tools. I’m currently working on multiple projects and happy to help others get started. Let’s make research accessible. Let’s stop waiting for permission. #MetaAnalysis #IndependentResearch #IMGResearch #SystematicReview #MedicalPublishing #ResidencyMatch #EvidenceBasedMedicine #OpenAccess #MentorlessButMotivated

NEW PAPER: Systematic Literature Reviews - Why I Rejected Your Review Systematic literature reviews (SLRs) are becoming increasingly popular in higher education research, but Journal of University Teaching and Learning Practice rejects most at the desk-reject stage. In this piece, I reflect on the most common methodological flaws we see and offer practical guidance for getting SLRs right. I focus on five key areas: 1️⃣ Well-scoped, answerable research questions 2️⃣ Transparent and replicable search strategies 3️⃣ Systematic screening and clear inclusion/exclusion 4️⃣ Trustworthy synthesis and quality appraisal 5️⃣ Implications that go beyond summary Hopefully this piece helps both emerging and experienced researchers sharpen their review methods, so much more robust systematic review appear across my desk — while also supporting editors and reviewers in setting clearer expectations. 🔗 Read the full commentary here: https://lnkd.in/ga-wfVz5

How do we reveal the true direction of an effect? Meta-analysis gives us the details. In our third session of the Systematic Review & Meta-Analysis series, in partnership with Schobot AI. We walked through a sequence of operational steps that form the internationally accepted framework for any high-quality meta-analysis: 1️⃣ Define the research question using PICO/PECO Transform the problem into measurable elements: Population, Intervention/Exposure, Comparison, and Outcome. 2️⃣ Include quantitative studies only We accept studies that provide: t-statistics (from t-tests or regression) • F-values • β coefficients • Odds ratios or risk ratios • Correlation coefficients (r) • Means, standard deviations, and sample sizes These values are then converted into a unified effect size, most commonly: ✔️r (correlation coefficient) ✔️Fisher’s Z ✔️SMD (Hedges g / Cohen’s d) ✔️log OR Such as experimental, quasi-experimental, longitudinal, and cross-sectional designs. 3️⃣ Pool results using Fixed or Random Effects models -Fixed-effect when studies share a highly similar context. -Random-effects when contexts differ Typically more appropriate in economic, managerial, and social research. → The output is a pooled estimate that reflects the true direction and size of the effect. 4️⃣ Assess heterogeneity Using: • Cochran’s Q to test for the presence of heterogeneity. • I² to quantify its magnitude (low – moderate – high). 5️⃣ Conduct a Risk of Bias assessment We applied tools to ensure evidence integrity: → RoB 2 for randomized trials → ROBINS-I for non-randomized studies → JBI Checklists for observational designs These tools evaluate study design, sampling, measurement quality, missing data, and control of confounding variables. 💡 Risk of bias assessment is critical because a single flawed study can distort the entire pooled outcome. 6️⃣ Evaluate the certainty of evidence using GRADE We explained how the GRADE framework strengthens transparency by rating evidence according to: ↳ Study quality ↳ Consistency of findings ↳ Precision ↳ Applicability ↳ Risk of bias The final rating classifies evidence as: High – Moderate – Low – Very Low Certainty Meta-analysis does not only tell you whether an effect exists. It reveals its direction, strength, consistency, and level of certainty after cutting through the noise of individual studies. Stay tuned! Next session, Hands-on implementation of all steps in R-Studio. 💾 Save this post to revisit later! ➕ Follow Dr. Saleh ASHRM for deeper insights