Quality and risk of bias appraisals of systematic reviews are inconsistent across reviewers and centers
Bibliographic record
Abstract
OBJECTIVE: The objective of the study was to evaluate the inter-rater and intercenter reliability, usability, and utility of A MeaSurement Tool to Assess systematic Reviews (AMSTAR), AMSTAR 2, and Risk Of Bias In Systematic reviews (ROBIS). STUDY DESIGN AND SETTING: This is a prospective evaluation using 30 systematic reviews of randomized trials, undertaken at three international centers. RESULTS: Reviewers completed AMSTAR, AMSTAR 2, and ROBIS in median (interquartile range) 15.7 (11.3), 19.7 (12.1), and 28.7 (17.4) minutes and reached consensus in 2.6 (3.2), 4.6 (5.3), and 10.9 (10.8) minutes, respectively. Across all centers, inter-rater reliability was substantial to almost perfect for 8/11 AMSTAR, 9/16 AMSTAR 2, and 12/24 ROBIS items. Intercenter reliability was substantial to almost perfect for 6/11 AMSTAR, 12/16 AMSTAR 2, and 7/24 ROBIS items. Intercenter reliability for confidence in the results of the review or overall risk of bias was moderate (Gwet's first-order agreement coefficient (AC1) 0.58, 95% confidence intervals [CI]: 0.30 to 0.85) to substantial (AC1 0.74, 95% CI: 0.30 to 0.85) for AMSTAR 2 and poor (AC1 -0.21, 95% CI: -0.55 to 0.13) to moderate (AC1 0.56, 95% CI: 0.30 to 0.83) for ROBIS. It is not clear whether using the appraisals of any tool as an inclusion criterion would alter an overview's findings. CONCLUSIONS: Improved guidance may be needed to facilitate the consistent interpretation and application of the newer tools (especially ROBIS).
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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 | 0.775 | 0.937 |
| Meta-epidemiology (narrow) | 0.003 | 0.006 |
| Meta-epidemiology (broad) | 0.018 | 0.023 |
| Bibliometrics | 0.035 | 0.032 |
| Science and technology studies | 0.005 | 0.010 |
| Scholarly communication | 0.018 | 0.014 |
| Open science | 0.008 | 0.013 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".