Risk of Adverse Pregnancy Outcomes by Prepregnancy Body Mass Index
Bibliographic record
Abstract
OBJECTIVE: To estimate the absolute risks of adverse maternal and perinatal outcomes based on small differences in prepregnancy body mass (eg, 10% of body mass or 10-20 pounds). METHODS: This population-based cohort study (N=226,958) was drawn from all singleton pregnancies in British Columbia (Canada) from 2004 to 2012. The relationships between prepregnancy body mass index (BMI) (as a continuous, nonlinear variable) and adverse pregnancy outcomes were examined using logistic regression models. Analyses were adjusted for maternal age, height, parity, and smoking in pregnancy. Adjusted absolute risks of each outcome are reported according to incremental differences in prepregnancy BMI and weight in pounds. RESULTS: A 10% difference in prepregnancy BMI was associated with at least a 10% lower risk of preeclampsia, gestational diabetes, indicated preterm delivery, macrosomia, and stillbirth. In contrast, larger differences in prepregnancy BMI (20-30% differences in BMI) were necessary to meaningfully reduce risks of cesarean delivery, shoulder dystocia, neonatal intensive care unit stay 48 hours or longer, and in-hospital newborn mortality. Prepregnancy BMI was not associated with risk of postpartum hemorrhage requiring intervention, severe maternal morbidity or maternal mortality, or spontaneous preterm delivery before 32 weeks of gestation. CONCLUSION: These results can inform prepregnancy weight loss counseling by defining achievable weight loss goals for patients that may reduce their risk of poor perinatal outcomes. LEVEL OF EVIDENCE: II.
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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 | 0.003 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".