Evaluation of AMSTAR to assess the methodological quality of systematic reviews in overviews of reviews of healthcare interventions
Bibliographic record
Abstract
BACKGROUND: Overviews of reviews (overviews) compile information from multiple systematic reviews (SRs) to provide a single synthesis of relevant evidence for decision-making. It is recommended that authors assess and report the methodological quality of SRs in overviews-for example, using A MeaSurement Tool to Assess systematic Reviews (AMSTAR). Currently, there is variation in whether and how overview authors assess and report SR quality, and limited guidance is available. Our objectives were to: examine methodological considerations involved in using AMSTAR to assess the quality of Cochrane and non-Cochrane SRs in overviews of healthcare interventions; identify challenges (and develop potential decision rules) when using AMSTAR in overviews; and examine the potential impact of considering methodological quality when making inclusion decisions in overviews. METHODS: We selected seven overviews of healthcare interventions and included all SRs meeting each overview's inclusion criteria. For each SR, two reviewers independently conducted AMSTAR assessments with consensus and discussed challenges encountered. We also examined the correlation between AMSTAR assessments and SR results/conclusions. RESULTS: Ninety-five SRs were included (30 Cochrane, 65 non-Cochrane). Mean AMSTAR assessments (9.6/11 vs. 5.5/11; p < 0.001) and inter-rater reliability (AC1 statistic: 0.84 vs. 0.69; "almost perfect" vs. "substantial" using the Landis & Koch criteria) were higher for Cochrane compared to non-Cochrane SRs. Four challenges were identified when applying AMSTAR in overviews: the scope of the SRs and overviews often differed; SRs examining similar topics sometimes made different methodological decisions; reporting of non-Cochrane SRs was sometimes poor; and some non-Cochrane SRs included other SRs as well as primary studies. Decision rules were developed to address each challenge. We found no evidence that AMSTAR assessments were correlated with SR results/conclusions. CONCLUSIONS: Results indicate that the AMSTAR tool can be used successfully in overviews that include Cochrane and non-Cochrane SRs, though decision rules may be useful to circumvent common challenges. Findings support existing recommendations that quality assessments of SRs in overviews be conducted independently, in duplicate, with a process for consensus. Results also suggest that using methodological quality to guide inclusion decisions (e.g., to exclude poorly conducted and reported SRs) may not introduce bias into the overview process.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.975 | 0.987 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.016 | 0.004 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.008 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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; both teacher heads 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".