Body Mass Index and Adverse Neonatal Outcomes in Women Undergoing Elective Cesarean: A Nationwide Cohort Study [25O]
Bibliographic record
Abstract
INTRODUCTION: To examine the association between pre-pregnancy body mass index (BMI) and neonatal outcomes among women who underwent elective cesarean section (CS). METHODS: We conducted a retrospective cohort study using data from the Center for Disease Control and Prevention (CDC) for all reported births by elective CS in 2011-2013. Women's pre-pregnancy BMI was categorized as underweight (“less than”18.5), normal (18.5-24.5), overweight (25-29.9), obese (30-39.9) and morbidly obese (“equal or above”40). Odds ratio (OR) and 95% confidence intervals (CIs) adjusted for baseline characteristics were calculated to estimate the neonatal risks in relation to pre-pregnancy BMI, using normal BMI as our reference. RESULTS: Our cohort was composed of 737,548 women with available BMI data, of whom 2.8% were underweight, 38.8% had normal BMI, 26.3% were considered overweight, 25.0% obese, and 7.2% morbidly obese. Infant mortality rates were of 4.2/1000 births for normal weight women, and 5.5/1000 births among the morbidly obese group (OR 1.43; 95% CI 1.25-1.64). A dose-dependent relationship between maternal pre-pregnancy BMI and assisted ventilation was seen. Furthermore, infants born to morbidly obese women were at significantly increased risk for assisted ventilation over 6 hours (OR 1.24; 95% CI 1.15-1.35) and admission to intensive care units (OR 1.17; 95% CI 1.13-1.21). Risk for adverse outcomes is higher with elective CS at earlier gestational age, and this effect is intensified with abnormally elevated maternal BMI. CONCLUSION: Increasing maternal pre-pregnancy BMI in women undergoing elective CS was found to be associated with progressively higher risks of adverse neonatal outcomes and mortality.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".