Evidence Appraisal
Everything on Aqrab tagged Evidence Appraisal — grouped into one landing page so readers can go deeper by problem family instead of bouncing around the archive blind.
CONSORT 2025 for Reviewers: A Practical Checklist for Interpretable Trials
A practical CONSORT 2025 reviewer checklist for randomized trials. Find the reporting gaps that change what you can infer about allocation, treatment, outcomes, missing data, and analysis.
Estimation Over Testing: Four Habits That Quietly Break Clinical Statistics
A practical critique of four entrenched habits in clinical biostatistics — significance testing where the real question is estimation, prediction accuracy scored far from the bedside, meta-analysis on autopilot, and the false precision of point estimates that assume away what cannot be tested — with an interactive explorer that reads one trial three ways and what to do instead.
PROBAST: When a Prediction Model Paper Looks Ready Before It Earns Trust
A practical guide to PROBAST for clinical researchers. Covers participant selection, predictor leakage, outcome definition, overfitting, calibration, and what reviewers should demand before trusting a clinical prediction model.
Indirectness in Clinical Evidence: When a Good Study Answers the Wrong Question
A practical guide to indirectness in clinical evidence for clinical researchers. Covers PICO mismatch, outdated comparators, surrogate outcomes, and what reviewers should demand before trusting an applicable-sounding conclusion.
Guideline Recommendation Strength: When “Strongly Recommend” Starts Outrunning the Evidence
A practical guide to recommendation strength in clinical guidelines. Covers certainty of evidence, benefit-harm tradeoffs, patient values, implementation burden, and what reviewers should demand before trusting a forceful recommendation.
Fragility Index: When One or Two Events Carry More Confidence Than They Should
A practical guide to the fragility index for clinical researchers. Covers event-flip sensitivity, loss to follow-up, effect-size context, and what reviewers should demand before trusting a barely significant trial.
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