Patterns of Gestational Weight Gain in Early Pregnancy and Risk of Gestational Diabetes Mellitus
Bibliographic record
Abstract
BACKGROUND: Despite a call to study the effect of weight gain pattern on development of gestational diabetes mellitus, few studies have correctly adjusted for independent effects of gain after the first trimester. We used a conditional percentile approach to model the independent association between first and second trimester weight gain trajectories and development of gestational diabetes. METHODS: We sampled women delivering singleton infants from 1998 to 2010 at Magee-Womens Hospital in Pittsburgh, PA, (n = 124,590) using a case-cohort design. We modeled weight gain trajectories in the first and second trimesters of pregnancy using conditional weight gain percentiles, and used multivariable logistic regression to assess independent associations of the trajectory with gestational diabetes. We studied associations separately by prepregnancy body mass index category. RESULTS: The final cohort included 806 women with gestational diabetes and 4,819 randomly sampled women who delivered without gestational diabetes. In normal-weight women, every SD increase in weight gain in the first trimester above her predicted gain was associated with a 23% increased odds of gestational diabetes (95% confidence interval: 0.2%, 51%). Second trimester gain trajectory was not associated with gestational diabetes (odds ratio: 1.1, [95% confidence interval: 0.9, 1.3]) although the direction of effect was positive. This pattern was similar in obese class I and II but not in overweight and obese class III women. CONCLUSIONS: An upward weight gain trajectory in the first trimester was positively associated with gestational diabetes for women of most prepregnancy BMI categories. Second trimester weight gain trajectory was not associated with gestational diabetes for any group.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".