Quality appraisal of systematic reviews on methods of labour induction: a systematic review
Bibliographic record
Abstract
PURPOSE: Induction of labour has become more common over the last decade, together with an increase in the number of systematic reviews of the subject. However, with multiple systematic reviews it is necessary to evaluate the methodological rigor to ensure the reliability of conclusions and recommendations for clinical practice. Therefore, the aim of this study was to appraise the quality of systematic reviews that examined the efficacy and/or safety of labour induction methods. METHODS: An electronic search of MEDLINE, Embase, and the Cochrane Library from 2000 to 2020 was conducted. Study selection, data extraction and quality assessment were conducted using A Measurement Tool to Assess Systematic Reviews (AMSTAR) by two independent reviewers, in duplicate. RESULTS: The search identified 387 publications, of which 48 studies (13%) met the a priori inclusion criteria. No significant relationships were found between study quality and number of citations, journal impact factor, or publication year. CONCLUSION: Methodological quality for systematic reviews on the induction of labour were ranked as moderate with no significant changes in quality over the past 2 decades. Publication characteristics are not significantly associated with methodological quality, indicating that healthcare professionals should critically appraise studies before applying them to practice.
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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 | 0.135 | 0.373 |
| Meta-epidemiology (narrow) | 0.005 | 0.005 |
| Meta-epidemiology (broad) | 0.034 | 0.029 |
| Bibliometrics | 0.022 | 0.018 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.010 | 0.007 |
| Open science | 0.005 | 0.007 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.004 | 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; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".