Methodological Quality of Systematic Reviews in Subfertility: A Comparison of Two Different Approaches
Bibliographic record
Abstract
BACKGROUND: Systematic reviews are used widely to guide health care decisions. Several tools have been created to assess systematic review quality. The measurement tool for assessing the methodological quality of systematic reviews known as the AMSTAR tool applies a yes/no score to eleven relevant domains of review methodology. This tool has been reworked so that each domain is scored based on a four point scale, producing R-AMSTAR. METHODS AND FINDINGS: We aimed to compare the AMSTAR and R-AMSTAR tools in assessing systematic reviews in the field of assisted reproduction for subfertility. All published systematic reviews on assisted reproductive technology, with the latest search for studies taking place from 2007-2011, were considered. Reviews that contained no included studies or considered diagnostic outcomes were excluded. Thirty each of Cochrane and non-Cochrane reviews were randomly selected from a search of relevant databases. Both tools were then applied to all sixty reviews. The results were converted to percentage scores and all reviews graded and ranked based on this. AMSTAR produced a much wider variation in percentage scores and achieved higher inter-rater reliability than R-AMSTAR according to kappa statistics. The average rating for Cochrane reviews was consistent between the two tools (88.3% for R-AMSTAR versus 83.6% for AMSTAR) but inconsistent for non-Cochrane reviews (63.9% R-AMSTAR vs. 38.5% AMSTAR). In comparing the rankings generated between the two tools Cochrane reviews changed an average of 4.2 places, compared to 2.9 for non-Cochrane. CONCLUSION: R-AMSTAR provided greater guidance in the assessment of domains and produced quantitative results. However, there were many problems with the construction of its criteria and AMSTAR was much easier to apply consistently. We recommend that AMSTAR incorporates the findings of this study and produces additional guidance for its application in order to improve its reliability and usefulness.
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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 | 0.630 | 0.834 |
| Meta-epidemiology (narrow) | 0.004 | 0.004 |
| Meta-epidemiology (broad) | 0.024 | 0.045 |
| Bibliometrics | 0.068 | 0.070 |
| Science and technology studies | 0.003 | 0.010 |
| Scholarly communication | 0.022 | 0.016 |
| Open science | 0.006 | 0.019 |
| Research integrity | 0.006 | 0.006 |
| Insufficient payload (model declined to judge) | 0.005 | 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".