Prediction Models for Determining the Success of Labor Induction: A Systematic Review [35C]
Bibliographic record
Abstract
INTRODUCTION: The purpose of this study was to systematically identify all derived and/or validated clinical prediction models for labor induction containing universally accessible factors and compare their performance to inform the development of a model that could be recommended for clinical practice. METHODS: Four databases were searched from inception to November, 2017 for studies that derived and/or validated clinical prediction models containing antenatal history and cervical examination. Risk-of-bias of included studies was assessed using the Prediction Study Risk of Bias Assessment Tool. In view of anticipated heterogeneity between studies, only descriptive analysis was possible. RESULTS: We identified 16 studies describing 14 prediction models derived between 1966 and 2018. Models varied with regard to participant inclusion, sample size, considered and included variables, endpoint definitions, study design and performance. Of the derived models, six were validated internally and three externally. Performance was most commonly measured using the area under the receiver operator characteristic curve, which ranged from 0.68 to 0.79, 0.67 to 0.77 and 0.61 to 0.73 for derived, internally validated and externally validated models, respectively. Studies' risk-of-bias ranged between studies fulfilling 36% to 86% of eligible items. CONCLUSION: No published model to determine the success of vaginal birth after labor induction can be currently recommended for clinical use. In order to improve performance and uptake, researchers should incorporate attitudes of women and care providers, assess clinical and resource implications and adhere to recommendations made by this systematic review before deriving and validating prediction models for determining the success of labor induction.
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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 | 0.048 | 0.200 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.010 | 0.021 |
| Bibliometrics | 0.015 | 0.012 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".