Tools used to appraise the quality of studies included in systematic reviews and meta-analyses in human genetics: a systematic review
Bibliographic record
Abstract
Quality assessment of primary studies is an essential component of systematic reviews (SRs). This methodological review systematically examines the choice, format and utilization of critical appraisal (CA) tools in SRs with or without meta-analyses in the field of human genetics. We searched MEDLINE, Embase, Web of Science, and PubMed up to January 2024. Two reviewers independently performed title, abstract, full-text screening and data extraction. This PROSPERO registered methodological review followed PRISMA guidelines. Meta-analysis and full-scale risk-of-bias assessment of SRs were not relevant. Among 149 randomly selected SRs, 136 mentioned CA tools (156 citations). Nineteen different generic tools constituted 71.2% of citations. NOS, QUADAS and the Cochrane risk-of-bias tool represented 36.5, 11.5, and 8.3% of tools, respectively. Ninety-three reviews stated following reporting guidelines, with 22 PRISMA checklists accessible. Detailed presentation of results was observed for 65.8% of generic and 37.8% of customized tools (p = 0.0013). Results for NOS were less often detailed than for other generic tools (p < 0.0001). Few SRs used CA results for study selection, data analysis, or discussion of findings. In conclusion, this first review of CA tools in human genetics SRs highlights a lack of transparency regarding utilization of CA tools and deficiencies in reporting of CA results.Registration: PROSPERO (CRD42023449349).
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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 | 0.316 | 0.656 |
| Meta-epidemiology (narrow) | 0.005 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.019 |
| Bibliometrics | 0.057 | 0.047 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.010 | 0.011 |
| Open science | 0.006 | 0.008 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".