Effects of Lifestyle Intervention of Maternal Gestational Diabetes Mellitus on Offspring Growth Pattern Before Two Years of Age
Bibliographic record
Abstract
Our group conducted a population-based randomized controlled trial (RCT) in Tianjin, China, which tested the effectiveness of intensive care (IC) versus usual care (UC) on adverse pregnancy outcomes among women with gestational diabetes mellitus (GDM), and found that with IC of GDM during pregnancy a 98-g birth weight reduction and a 34% risk reduction in macrosomia were achieved (1). We further followed offspring born to women enrolled in the RCT from 1 month to 2 years after delivery to test whether IC of GDM during pregnancy modified early-life growth of offspring born to Chinese women with GDM. The study settings, population, and design have been previously described (1). Briefly, a total of 19,847 pregnant women were screened for GDM with a glucose challenge test between the 24th and 28th weeks of pregnancy, and 2,921 women with a glucose challenge test level ≥7.8 mmol/L underwent the standard oral glucose tolerance test. Of them, 1,440 women with GDM were identified based on the International Association of Diabetes and Pregnancy Study Group (IADPSG) criteria (2) and 706 eligible women were randomized to either IC or UC group and completed the trial. The UC included one group diabetes education session at diagnosis of GDM, while the IC included additional two individualized diabetes education sessions at the …
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".