Quality Appraisal of Systematic Reviews on the Efficacy and Safety of Labour Induction Methods: Systematic Review.
Bibliographic record
Abstract
Background: The induction of labour has been increasing over the last decade. It is most often indicated when the safety of the baby or mother may be compromised. Objectives: This study aims to assess the quality of systematic reviews that examined the efficacy and/or safety of various methods of induction of labour. Search Strategy: An electronic database search of MEDLINE, Embase, and the Cochrane Library was conducted. The search strategy can be found in the online supplement. Selection Criteria: Systematic reviews that examined various methods of induction of labour. Inclusion and exclusion criteria can be found in the main text. Data Collection and Analysis: Study characteristics such as journal and impact factor, year of publication, source of funding, citation rate, etc. were retrieved. Quality assessment was conducted using A Measurement Tool to Assess Systematic Reviews (AMSTAR). Main Results: There were no significant relationships between mean AMSTAR score and number of citations (p=0.0875, r=0.25; 95% CI, -0.04 to 0.50), journal impact factor (p=0.2959, r=-0.15; 95% CI, -0.42 to 0.14), or publication year (p=0.5827, r=0.08; 95% CI, -0.20 to 0.36). Cochrane studies on average scored higher than non-Cochrane studies (p=0.01). No significant differences were detected between the AMSTAR scores of government and non-government funded studies (p=0.34). Conclusions: Better adherence to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses statement and for peer reviewers to appraise new systematic reviews with methodological assessment tools would enhance confidence in review conclusions.
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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 | 0.090 | 0.357 |
| Meta-epidemiology (narrow) | 0.003 | 0.003 |
| Meta-epidemiology (broad) | 0.017 | 0.013 |
| Bibliometrics | 0.024 | 0.023 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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; 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".