Body mass index trajectories and time in target range after delivery and long‐term type 2 diabetes risk in women with a history of gestational diabetes mellitus
Bibliographic record
Abstract
AIMS: This study aims to determine whether postpartum body mass index (BMI) trajectories and its time in target range (TTR) are associated with long-term type 2 diabetes risk in women with a history of gestational diabetes mellitus (GDM). MATERIALS AND METHODS: ). The associations of BMI trajectories and TTR with type 2 diabetes risk were analysed using multivariable Cox modelling. RESULTS: Five distinct trajectories of postpartum BMI were identified. Compared with low-stable class, the multivariable-adjusted hazard ratios of type 2 diabetes were 2.02 (95% confidence interval 0.99-4.10) for median-stable class, 3.01 (1.17-7.73) for high-stable class, 2.15 (0.63-7.38) for U-shape class and 7.15 (2.08-24.5) for inverse U-shape class (p for trend = 0.012), respectively. Multivariable-adjusted hazard ratios of type 2 diabetes associated with postpartum BMI TTR of 100%, >43.4%-<100%, >0%-≤43.4% and 0% were 1.00, 1.84 (0.72-4.73), 2.75 (1.23-6.15) and 2.31 (1.05-5.08) (p for trend = 0.039), respectively. CONCLUSIONS: Postpartum BMI trajectories of high-stable and inverse U-shape class as well as lower TTR were associated with an increased risk of type 2 diabetes among women with a history of GDM. Reducing BMI to a normal range in the early postpartum period and maintaining stable over time could attenuate the development of long-term type 2 diabetes.
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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 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".