856A gestational age specific prognostic model for adverse perinatal events in obese women
Bibliographic record
Abstract
Abstract Background Obesity is one of the most preventable pre-pregnancy risk factors for adverse perinatal events. Despite this, there are few body-mass-index (BMI) specific prognostic models for timing of delivery associated with the lowest number of adverse perinatal events. Our aim was to build a predictive model to quantify gestational age-specific rates of adverse birth outcomes in obese women with and without additional risk factors. Methods All singleton births at ≥ 34 weeks’ gestation in British Columbia, Canada, 2008-2017 (n = 283,697) were included and data were obtained from the British Columbia Perinatal Database Registry. A multivariable Cox proportional hazards model including demographic and obstetric risk factors was used to estimate gestational age specific risk of composite perinatal mortality and severe morbidity. Results Among all women, 13.1% were obese (pre-pregnancy BMI ≥30m/kg2), 60.1% had normal BMI (18.5-24.9 m/kg2). In high-risk obese women (nulliparous with chronic hypertension, and diabetes), adjusted outcome rates (per 1000 ongoing pregnancies) were 7.5 at 34-36 weeks, 20.4 at 37-39 weeks, and 83.5 at ≥ 40 weeks’ gestation. In all obese women, the rates were 1.93, 6.27, and 18.5 per 1000 ongoing pregnancies, respectively. In contrast, on average these rates were 1.14, 4.03 and 11.6 per 1000 ongoing pregnancies, respectively, among women with normal BMI. Conclusions Obese women are at increased risk of poor perinatal outcomes at all gestational ages. These risks are compounded by other conditions known to effect perinatal outcomes. Key messages Obese women require specific guidelines for timing of optimal delivery.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| 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) | 0.007 | 0.001 |
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".