Association between Elevated Pre-pregnancy BMI and Outcomes of Labour Induction: A Population-Based Retrospective Cohort Study
Bibliographic record
Abstract
Objective To evaluate the impacts of elevated pre-pregnancy body mass index (BMI) on the outcomes of labour induction, especially for those in higher BMI categories. Methods Population-based retrospective cohort study using data from the Perinatal Program Newfoundland and Labrador (PPNL) database from 2002-2023. Mode of delivery was the primary outcome of interest. Composite secondary maternal and neonatal outcomes, and a severity-weighted composite outcome, were calculated. Outcomes were analyzed using logistic and Poisson regressions, adjusting for patient age, gestational age, parity and smoking status. Adjusted risks or rates with associated 95% confidence intervals (CI) were reported for each outcome. Outcome data were used to produce a clinical risk calculator. Results Analyses included 16 808 records. The risks of unplanned and emergent cesarean delivery (CD) increased with BMI in a dose-dependent fashion. For example, the adjusted risks of unplanned and emergent CD at a BMI of 20.0kg/m 2 were 22.5% (95% CI 20.8–24.3) and 17.0% (95% CI 15.5–18.6), respectively. In comparison, these risks increased to 59.8% (95% CI 44.8–73.3) and 49.5% (95% CI 34.7–64.3), respectively, at a BMI of 65.0kg/m 2 . The opposite trend was observed for spontaneous vaginal delivery. The severity-weighted composite outcome was lowest at a BMI of 19.0kg/m 2 (17.5, 95% CI 17.0–17.9) and increased to a maximum at a BMI of 48.0kg/m 2 (33.9, 95% CI 33.3–34.5). Conclusion Elevated BMI increases risks of unplanned and emergent CD in those who undergo IOL. Our risk calculator can provide additional information for patient assessment and counselling.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".