Lactation intensity and duration to postpartum diabetes and prediabetes risk in women with gestational diabetes
Bibliographic record
Abstract
Abstract Objective To investigate the association of lactation intensity and duration with postpartum diabetes and prediabetes risks among Chinese women with a history of gestational diabetes (GDM). Methods We included 1260 women with a history of GDM who participated in the whole population's GDM universal screening survey by using the 1999 World Health Organization's criteria. Lactation intensity and lactation duration were collected by a standardized questionnaire. Postpartum diabetes and prediabetes risk were confirmed by an oral glucose tolerance test. Results During a mean postpartum period of 3.65 years, we identified 114 cases of diabetes and 417 cases of prediabetes. The multivariable‐adjusted hazard ratios based on different lactation intensity (exclusive formula, mixed feeding, and exclusive lactation) were 1.00, 0.68, 0.45 for diabetes (Ptrend = 0.008), and 1.00, 0.74, and 0.61 for prediabetes (Ptrend = 0.006), respectively. The multivariable‐adjusted hazard ratios associated with different lactation duration (none, 0‐6 months, 6‐12 months, 12‐18 months, and ≥18 months) were 1.00, 0.66, 0.42, 0.66, and 0.25 for diabetes (Ptrend = 0.013), and 1.00, 0.82, 0.62, 0.67, and 0.63 for prediabetes (Ptrend = 0.021), respectively. A restricted cubic spline curve showed a graded inverse association of lactation duration with the risks of diabetes and prediabetes (Ptrend < 0.001). Conclusions Higher‐lactation intensity and longer‐lactation duration were significantly associated with lower risks of postpartum diabetes and prediabetes among Chinese women with a history of GDM.
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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 | 0.000 | 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".