An assessment of the quality of current clinical meta-analyses
Bibliographic record
Abstract
BACKGROUND: The objective of this study was to assess the overall quality of study-level meta-analyses in high-ranking journals using commonly employed guidelines and standards for systematic reviews and meta-analyses. METHODS: 100 randomly selected study-level meta-analyses published in ten highest-ranking clinical journals in 2016-2017 were evaluated by medical librarians against 4 assessments using a scale of 0-100: the Peer Review of Electronic Search Strategies (PRESS), Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA), Institute of Medicine's (IOM) Standards for Systematic Reviews, and quality items from the Cochrane Handbook. Multiple regression was performed to assess meta-analyses characteristics' associated with quality scores. RESULTS: The overall median (interquartile range) scores were: PRESS 62.5(45.8-75.0), PRISMA 92.6(88.9-96.3), IOM 81.3(76.6-85.9), and Cochrane 66.7(50.0-83.3). Involvement of librarians was associated with higher PRESS and IOM scores on multiple regression. Compliance with journal guidelines was associated with higher PRISMA and IOM scores. CONCLUSION: This study raises concerns regarding the reporting and methodological quality of published MAs in high impact journals Early involvement of information specialists, stipulation of detailed author guidelines, and strict adherence to them may improve quality of published meta-analyses.
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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 | 0.483 | 0.747 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.018 |
| Bibliometrics | 0.023 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.004 | 0.004 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".