Prepregnancy Body Mass Index as a Significant Predictor of Total Gestational Weight Gain and Birth Weight
Bibliographic record
Abstract
Purpose: We aimed to describe adherence to gestational weight gain (GWG) recommendations and identify determinants of excessive GWG in a sample of women from Quebec, Canada. Methods: Data were collected from the multi-centre 3D (Design, Develop, Discover) pregnancy cohort study, which included women who delivered between May 2010 and August 2012 at 9 obstetrical hospitals in Quebec, Canada. GWG was calculated for 1145 women and compared to the 2009 Institute of Medicine (IOM) recommendations. Results: Overall, 51% of participants exceeded the recommendations. Approximately 68% of women with obesity gained weight in excess of the IOM recommendations. The corresponding numbers were 75%, 44%, and 27% in overweight, normal weight, and underweight women, respectively. A prepregnancy BMI of 25 kg/m 2 or more was the only significant predictor of exceeding GWG recommendations (OR 3.35, 95% CI 2.44–4.64) in a multivariate model. Birth weight was positively associated with GWG. GWG and prepregnancy BMI could explain 3.13% and 2.46% of the variance in birth weight, respectively. Conclusion: About half of women exceeded GWG recommendations, and this was correlated with infant birth weight. This reinforces the need to develop and evaluate strategies, including nutritional interventions, for pregnant women to achieve optimal GWG.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".