Optimal Plan for Delivery in Women with Obesity: A Large Population-based Retrospective Cohort Study Using the Better Outcomes Registry and Network (BORN) Database
Bibliographic record
Abstract
Objective: To discern the optimal plan for delivery in nulliparous women with obesity at term gestation. Design: Large population-based retrospective cohort study Setting: Maternity hospitals in Ontario, Canada Population: Nulliparous women with obesity (BMI>30) with live, singleton, uncomplicated term gestations (37+0 to 41+6 weeks) between April 1st, 2012 and March 31st, 2019 Methods: Women were divided by plan for delivery (expectant management, induction of labour and no-labour caesarean section). The outcomes of interest were adverse delivery outcomes. Analyses were conducted using multivariable regression models. Analyses were stratified by each week of gestational age and by obesity class. Main Outcome Measures: The primary outcome was the Adverse Outcome Index (AOI), a binary composite of 10 maternal and neonatal adverse events. The Weighted Adverse Outcome Score (WAOS) was the secondary outcome. It provides a weighted score of each adverse event included in the AOI. Results: No-labour caesarean section reduced the risk of adverse delivery outcome by 41% (aRR 0.59, 95%CI [0.50, 0.70]) compared to expectant management at term gestation. There was no statistically significant difference in adverse birth outcomes when comparing induction of labour to expectant management (aRR 1.03, 95% CI [0.96, 1.10]). The greatest benefit to no-labour caesarean section was observed in the reduction of adverse neonatal events (aRR 0.70, 95% CI [0.57, 0.87]) particularly at 39 weeks of gestation. Conclusion: In women with obesity, no-labour caesarean section reduces adverse birth outcomes. Funding: Canadian Institute for Health Research (CIHR) (#MFM146444). Keywords: Plan for delivery, Induction of Labour, Caesarean Section, Obesity
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".