The association between offspring birthweight and future risk of maternal diabetes: A population‐based study
Bibliographic record
Abstract
AIMS: As an indicator of maternal cardiometabolic health, newborn birthweight may be an important predictor of maternal type 2 diabetes mellitus (diabetes). We evaluated the relation between offspring birthweight and onset of maternal diabetes after pregnancy. METHODS: This retrospective cohort study used linked population-based health databases from Ontario, Canada. We included women aged 16-50 years without pre-pregnancy diabetes, and who had a live birth between 2006 and 2014. We used Cox proportional hazard regression to evaluate the association between age- and sex-standardized offspring birthweight percentile categories and incident maternal diabetes, while adjusting for maternal age, parity, year, ethnicity, gestational diabetes (GDM) and hypertensive disorders of pregnancy (HDP). Results were further stratified by the presence of GDM in the index pregnancy. RESULTS: Of 893,777 eligible participants, 14,329 (1.6%) women were diagnosed with diabetes over a median (IQR) of 4.4 (1.5-7.4) years of follow-up. There was a continuous positive relation between newborn birthweight above the 75th percentile and maternal diabetes. Relative to a birthweight between the 50th and 74.9th percentiles, women whose newborn had a birthweight between the 97th and 100th percentiles had an adjusted hazards ratio (aHR) of diabetes of 2.30 (95% CI 2.16-2.46), including an aHR of 2.01 (95% CI 1.83-2.21) among those with GDM, and 2.59 (2.36-2.84) in those without GDM. CONCLUSIONS: A higher offspring birthweight signals an increased risk of maternal diabetes, offering another potentially useful way to identify women especially predisposed to 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 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".