Prepregnancy body mass index and weight change on postpartum diabetes risk among gestational diabetes women
Bibliographic record
Abstract
OBJECTIVE: To evaluate the effects of prepregnancy BMI and weight change from prepregnancy to postpartum on postpartum type 2 diabetes (T2D) risk among women with gestational diabetes (GDM). METHODS: A retrospective cohort study in 1,263 GDM women at 1-5 years after delivery was performed. Cox proportional hazards regression models were used to evaluate the association of prepregnancy BMI and weight change with T2D and prediabetes risks. RESULTS: The multivariable-adjusted hazard ratios based on different levels of prepregnancy BMI (<23, 23-24.9, 25-29.9, and ≥30 kg/m(2) ) were 1.00, 1.77, 2.35, and 6.54 (Ptrend < 0.001) for incident T2D, and 1.00, 1.46, 1.87, and 1.79 (Ptrend < 0.001) for incident prediabetes, respectively. Compared with women with stable weight (±3 kg), those with weight gain ≥7 kg had an 86% and a 32% increased risk of diabetes and prediabetes, and those with weight loss ≥3 kg had a 45% decreased risk of prediabetes. The positive associations of prepregnancy BMI with incident diabetes and prediabetes risk were persistent in women with different levels of weight change (<3 kg and ≥3 kg). CONCLUSION: Prepregnancy obesity and excessive weight gain from prepregnancy to postpartum increase postpartum diabetes and prediabetes risks among GDM women.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.000 |
| 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".