Using Non-inferiority Margins to Define Optimal Gestational Weight Gain Ranges Based on Reducing the Risk of Occurrence of Adverse Neonatal Outcomes
Bibliographic record
Abstract
To identify optimal gestational weight gain (GWG) ranges to prevent adverse neonatal outcomes based on the new Brazilian GWG charts. Data from 9,294 women from the Brazilian Maternal and Child Nutrition Consortium and Birth in Brazil study were used. Women aged ≥18 years, free of hypertensive disorders, diabetes in pregnancy, or diseases affecting GWG, were selected. Total GWG was calculated as last measured prenatal weight minus self-reported pre-pregnancy weight. Total GWG was standardized to gestational age-specific z scores according to the Brazilian GWG charts. A composite outcome was defined as the occurrence of any of small-for-gestational-age birth (SGA, birthweight < 10th percentile), large-for-gestational-age birth (LGA > 90th percentile) according to INTERGROWTH-21st charts, or preterm birth (birth < 37 weeks). We weighted each outcome in a composite index using previously-published weights to account for its relative seriousness. Logistic and Poisson regressions were performed with GWG z scores as exposure and independent outcomes and the composite outcome, respectively. Models were adjusted for maternal age, education, pre-pregnancy BMI, and smoking during pregnancy. GWG ranges associated with the lowest risk of the composite outcome were identified using the non-inferiority margins approach (20%). The median total GWG was 12.5 kg (IQR 9–16), and 6.2% of the neonates were SGA, 16.6% LGA, and 10.5% were preterm. Higher GWG z scores were associated with an increase in LGA probabilities and preterm birth compared with neonates born with appropriate weight and ≥37 weeks, respectively. Lower z scores were associated with an increase in SGA probability. The non-inferiority margins analysis showed that to prevent the occurrence of these adverse outcomes, women with underweight, normal-weight, overweight, or obesity should gain between 6.5–14.1 kg, 6.4–13.8 kg, 2.2–12.1 kg, and –2.2–8.9 kg, respectively. Total GWG ranges associated with lower risk of adverse neonatal outcomes were identified using non-inferiority margins. The next step must incorporate maternal outcomes in this analysis. Brazilian National Research Council, Brazilian Ministry of Health, Bill and Melinda Gates Foundation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".