A Predictive Model for Cesarean Among Low‐Risk Nulliparous Women in Spontaneous Labor at Hospital Admission
Bibliographic record
Abstract
BACKGROUND: To determine if maternal characteristics measurable upon admission to hospital predict cesarean among low-risk spontaneously laboring nulliparous women. METHODS: We undertook a secondary analysis of data from a clinical trial of early labor support for nulliparous women carrying a singleton fetus in cephalic presentation at 37-41 weeks of gestation in British Columbia, Canada. Study participants did not have any discernible risk factors for cesarean at the onset of labor. We developed a prediction model using logistic regression from a sample of 1,302 participants. Internal validation of the model was accomplished by 10-fold cross validation, after which probability scores were calculated based on the mean logistic regression model. To determine the accuracy of our predictive model, we calculated the specificity and sensitivity and the area under the receiver operating curve. RESULTS: Advanced maternal age, shorter maternal height, greater gestational age, perception of labor lasting more than 24 hours, and mild or moderate contractions, less cervical dilation, and higher fetal station at time of hospital admission independently predicted cesarean. The C-statistic for the predictive model was 0.71 (0.64-0.75) and the sensitivity and specificity of the model were 0.80 (95% CI 0.76-0.84) and 0.48 (95% CI 0.44-0.52), respectively. CONCLUSIONS: Among nulliparous women without apparent risk for cesarean at the time of hospital admission, cesarean delivery can be predicted with 70 percent accuracy using routinely collected information. Tailoring intrapartum care to promote vaginal birth according to a prediction model for cesarean risk deserves further study among apparently low risk women.
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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 | 0.004 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".