Risk of gestational diabetes mellitus in relation to early pregnancy and gestational weight gain before diagnosis: A population‐based cohort study
Bibliographic record
Abstract
INTRODUCTION: Gestational diabetes mellitus (GDM) is a common pregnancy complication associated with adverse consequences for the mother and offspring in both short and long term. The aim of this study was to investigate associations between risk of GDM and gestational weight gain in early pregnancy and before diagnosis. MATERIAL AND METHODS: Our population-based cohort study included 131 164 singleton pregnancies in the Stockholm-Gotland region in Sweden from 2008 through 2013. The exposures were weight gain in early pregnancy (<22 weeks) and weight gain before diagnosis, standardized into gestational age-specific z scores. The outcome was GDM. We used logistic regression models with a generalized estimating equations method to estimate odds ratios with 95% confidence intervals for GDM, stratified by early-pregnancy body mass index (BMI) category. RESULTS: Above average weight gain before diagnosis (z score >0) was associated with increased risk of GDM among all BMI groups except for obese III. Early gestational weight gain above average was associated with increased risk for GDM in overweight women. Below average weight gain before diagnosis (z score <0) was only associated with decreased risk of GDM in obese III. Early gestational weight gain below average was associated with reduced risks of GDM in obese class I, II, and III women. CONCLUSIONS: The risk of GDM increased with higher weight gain before diagnosis in all BMI groups except obese class III, whereas the risk was reduced with lower weight gain before diagnosis in obese III women only. The risk of GDM increased with higher early gestational weight gain in overweight women, while the risk was reduced with lower early gestational weight gain among obese women. Obese women may benefit from lower weight gain, especially in early pregnancy.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| 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) | 0.001 | 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".