Bibliographic record
Abstract
INTRODUCTION: The aim of this study is to determine how the Bishop Score (BS) fares as a measurement tool. METHODS: The literature was reviewed to identify publications reporting the derivation and measurement properties of the BS. Its sensibility and reliability were assessed using 19/21 Feinstein principles and weighted kappa respectively. RESULTS: BS was derived with the intention of selecting multiparous women most likely to have a vaginal birth within four hours of commencing induction of labor (IOL) based on a score ≥9, and not for predicting the success of IOL. With regard to sensibility, the score performed well on 12/19 attributes encompassing domains of purpose and framework, comprehensibility and ease of usage; moderately on 1/19 attribute related to face validity and poorly on 6/19 attributes related to replicability, suitability and content validity. Areas of greatest concern included omissions of important variables known to influence the success of IOL as well as inclusion and equal weighting of five highly correlated components. Kappa values varied between 0.35 and 0.69. CONCLUSION: Although intended to be a discriminative index in multiparous women, BS is widely used as a prediction tool to determine the success of IOL in all women. Despite concerns regarding its derivation, sensibility and reliability, meta-analyses show that it is the best available tool to determine the success of IOL. With recent studies showing that cervical favorability is a poor determinant of the success of IOL, BS needs to be replaced by a prediction tool derived using sound statistical principles and validated in global settings.
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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 | 0.022 | 0.055 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| 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; 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".