Exploring the methodological quality and risk of bias in 200 systematic reviews: A comparative study of ROBIS and AMSTAR-2 tools
Bibliographic record
Abstract
AMSTAR-2 (A Measurement Tool to Assess Systematic Reviews, version 2) and ROBIS are tools used to assess the methodological quality and the risk of bias in a systematic review (SR). We applied AMSTAR-2 and ROBIS to a sample of 200 published SRs. We investigated the overlap in their methodological constructs, responses by item, and overall, percentage agreement, direction of effect, and timing of assessments. AMSTAR-2 contains 16 items and ROBIS 24 items. Three items in AMSTAR-2 and nine in ROBIS did not overlap in construct. Of the 200 SRs, 73% were low or critically low quality using AMSTAR-2, and 81% had a high risk of bias using ROBIS. The median time to complete AMSTAR-2 and ROBIS was 51 and 64 minutes, respectively. When assessment times were calibrated to the number of items in each tool, each item took an average of 3.2 minutes per item for AMSTAR-2 compared to 2.7 minutes for ROBIS. Nine percent of SRs had opposing ratings (i.e., AMSTAR-2 was high quality while ROBIS was high risk). In both tools, three-quarters of items showed more than 70% agreement between raters after extensive training and piloting. AMSTAR-2 and ROBIS provide complementary rather than interchangeable assessments of systematic reviews. AMSTAR-2 may be preferable when efficiency is prioritized and methodological rigour is the focus, whereas ROBIS offers a deeper examination of potential biases and external validity. Given the widespread reliance on systematic reviews for policy and practice, selecting the appropriate appraisal tool remains crucial. Future research should explore strategies to integrate the strengths of both instruments while minimizing the burden on assessors.
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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 | 0.459 | 0.799 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.009 | 0.017 |
| Bibliometrics | 0.040 | 0.042 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.004 | 0.010 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".