Validating a predictive model for caesarean section in low-risk nulliparous pregnancies
Bibliographic record
Abstract
PROBLEM: Caesarean birth (CS) rates are steadily increasing. BACKGROUND: In 2017 Janssen et al. developed a model which could predict CB in nulliparous healthy woman with 71 % accuracy based on factors measurable on admission to the hospital. AIM: To validate the predictive model for risk of caesarean birth among low-risk, nulliparous women in a new setting. METHODS: A retrospective chart study in Abbotsford Regional Hospital (British Columbia, Canada) of healthy nulliparous women in spontaneous labour, at term, with a singleton fetus in cephalic position. Sociodemographic, pregnancy and labour-related characteristics were collected and independent predictors of CS were determined using multivariate logistic regression. The Janssen model was tested in the Abbotsford sample and additionally novel predictors were tested in an effort to improve the model. The area under the ROC curve (C-statistic) was computed and model calibration, sensitivity and specificity evaluated for the final model. FINDINGS AND DISCUSSION: Of 348 women, 106 (30.5 %) had a CB. Applying the Janssen predictive model to the Abbotsford data resulted in a C-statistic of 0.77. No new predictors were added to the model. The mean predicted risk score for CS in the cohort was 0.30 ± 0.20. A risk score cut-off of 0.32 was determined resulting in a sensitivity and specificity of 69 %. The model had acceptable calibration. CONCLUSION: A model with variables easily accessible at admission can predict caesarean birth in nulliparous women. The results from this study can guide provision of more intensive care during labour to women at higher risk, with the overall goal of reducing CB rates.
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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 | 0.009 | 0.031 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".