Maternal insulin resistance and maternal β‐cell function during pregnancy for offspring overweight before 2 years of age among women with gestational diabetes
Bibliographic record
Abstract
OBJECTIVE: To explore associations of maternal insulin resistance and β-cell dysfunction with offspring overweight before 24 months of age among children of Chinese women with gestational diabetes mellitus (GDM). METHODS: Offspring of women with GDM (n = 901) who were enrolled in a lifestyle trial during pregnancy were followed up to 24 months of age in Tianjin, China. Restricted cubic spline analysis was performed to examine full-range associations of maternal homeostatic model assessment of insulin resistance (HOMA-IR) and β-cell function (HOMA-%β) with childhood overweight. Logistic regression was performed to obtain the odds ratios (ORs) and 95% confidence interval (CI) of maternal high HOMA-IR and low HOMA-%β at diagnosis of GDM for offspring overweight within 12 months of age and at 13-24 months of age. RESULTS: Maternal high HOMA-IR was associated with an increased risk of offspring being overweight within 12 months of age and at 13-24 months of age (OR: 1.71, 95%CI: 1.12-2.62 & 1.89, 1.13-3.17, respectively). Maternal low HOMA-%β was associated with an increased risk of offspring being overweight at 13-24 months of age (1.64, 1.05-2.55). CONCLUSIONS: Both maternal increased insulin resistance and decreased β-cell function at diagnosis of GDM were associated with elevated risk of offspring overweight in early childhood among Chinese women with 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 | 0.001 |
| 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".