Maternal anthropometric risk factors for caesarean delivery before or after onset of labour
Bibliographic record
Abstract
OBJECTIVE: To quantify the effects of pre-pregnancy body mass and gestational weight gain, above and beyond their known effects on birthweight, on the risk of primary and repeat caesarean delivery performed before or after the onset of labour. DESIGN: Hospital-based historical cohort study. SETTING: Canadian university-affiliated hospital. POPULATION: A total of 63 390 singleton term (> or = 37 weeks gestation) infants with cephalic presentation. METHODS: We studied prospectively archived deliveries at the Royal Victoria Hospital in Montreal, Canada, from 1 January 1978 to 31 March 2001 using multiple logistic regression models to estimate relative odds of caesarean delivery. MAIN OUTCOME MEASURE: Caesarean delivery, primary or repeat and before or after the onset of labour. RESULTS: Pregravid obesity (body mass index > or = 30 kg/m2) increased the likelihood of primary caesarean delivery before (OR = 2.01, 95% CI 1.39-2.90) and after (OR = 2.12, 95% CI 1.86-2.42) the onset of labour. High net rate of gestational weight gain (> 0.50 kg/week) increased the risk but only after labour onset (OR = 1.40, 95% CI 1.23-1.60). Among women with a previous caesarean, high weight gain modestly increased risk but only before labour (OR = 1.38, 95% CI 1.04-1.83), whereas obesity increased the risk of caesarean delivery both before (OR = 1.85, 95% CI 1.44-2.37) and after (OR = 1.96, 95% CI 1.11-3.47) labour onset. Increased risks of macrosomia accounted for the association between pregravid adiposity and repeat caesarean delivery performed after but not before the onset of labour. CONCLUSIONS: Pregravid obesity increases the risk of caesarean delivery both before and after the onset of labour and both with and without a history of caesarean.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".