Agreeability testing of AMSTAR-PF, a tool for quality appraisal of systematic reviews of prognostic factor studies
Bibliographic record
Abstract
Abstract Background This paper details initial testing of the agreeability and usability of a novel quality appraisal tool for systematic reviews of prognostic factor studies: AMSTAR-PF. Methods Fourteen appraisers each assessed eight systematic reviews using AMSTAR-PF. Their ratings for each question and each article were compared, with interrater, inter-pair and intrapair agreeability calculated using Gwet’s agreement coefficient. Time of use and time to reach consensus were also recorded. Results Interrater agreement averaged 0.59 (range, 0.21-0.90), inter-pair 0.61 (range 0.24-0.91) and intrapair 0.75 (range 0.45-0.95) across the domains, with agreement for the overall rating 0.46 (95%CI 0.30-0.62) for interrater, 0.46 (95%CI 0.17-0.74) for inter-pair, and 0.68 (range of averages 0.22-1.00) for intrapair agreement. The majority (60.7%) of intrapair ratings were identical, with 94.6% of final ratings either identical or only one category different for the overall appraisal. The time taken to appraise a study with AMSTAR-PF improved with use and averaged around 34 minutes after the first two appraisals. Conclusions Despite some variance in agreeability for different domains and between different appraisers, the testing results suggest that AMSTAR-PF has clear utility for appraising the quality of systematic reviews of prognostic factor studies.
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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 | 0.672 | 0.870 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.005 | 0.016 |
| Bibliometrics | 0.018 | 0.015 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.006 | 0.008 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.004 | 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".