Critical Appraisal of a Systematic Review: A Concise Review
Bibliographic record
Abstract
OBJECTIVES: Concise definitive review of how to read and critically appraise a systematic review. DATA SOURCES: None. STUDY SELECTION: Current literature describing the conduct, reporting, and appraisal of systematic reviews and meta-analyses. DATA EXTRACTION: Best practices for conducting, reporting, and appraising systematic review were summarized. DATA SYNTHESIS: A systematic review is a review of a clearly formulated question that uses systematic and explicit methods to identify, select, and critically appraise relevant original research, and to collect and analyze data from the studies that are included in the review. Critical appraisal methods address both the credibility (quality of conduct) and rate the confidence in the quality of summarized evidence from a systematic review. The A Measurement Tool to Assess Systematic Reviews-2 tool is a widely used practical tool to appraise the conduct of a systematic review. Confidence in estimates of effect is determined by assessing for risk of bias, inconsistency of results, imprecision, indirectness of evidence, and publication bias. CONCLUSIONS: Systematic reviews are transparent and reproducible summaries of research and conclusions drawn from them are only as credible and reliable as their development process and the studies which form the systematic review. Applying evidence from a systematic review to patient care considers whether the results can be directly applied, whether all important outcomes have been considered, and if the benefits are worth potential harms and costs.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.211 | 0.536 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.013 | 0.010 |
| Bibliometrics | 0.023 | 0.017 |
| Science and technology studies | 0.003 | 0.006 |
| Scholarly communication | 0.012 | 0.014 |
| Open science | 0.005 | 0.008 |
| Research integrity | 0.010 | 0.013 |
| Insufficient payload (model declined to judge) | 0.025 | 0.012 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; the direct Gemma label and the distilled Codex classifier agree on what is shown here.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".