Prenatal, Obstetric and Perinatal Factors Associated With the Development of Childhood-Onset Type 2 Diabetes
Bibliographic record
Abstract
OBJECTIVE: To explore associations among prenatal, obstetric and perinatal factors and the development of childhood-onset type 2 diabetes. METHODS: This retrospective, case-control study utilized administrative data housed at the Manitoba Centre for Health Policy. De-identified health records were examined from a sample of 270 children (aged 10 to 17 years at time of diagnosis) with type 2 diabetes and 1341 children without type 2 diabetes matched for age, sex and geographic location. Patients and control subjects were linked to their de-identified biological mothers' health records. Prenatal, obstetric and perinatal factors were investigated. Univariate and multivariable conditional regression analyses were conducted to identify key factors associated with the development of type 2 diabetes in children. RESULTS: The mean age at diagnosis was 13.1 years, and 61% of patients were girls. The majority (71.1%) of children with type 2 diabetes resided in rural areas. Exposure to maternal pregestational diabetes increased the odds of childhood-onset type 2 diabetes nearly 6-fold, and exposure to gestational diabetes carried a 4-fold increased risk. Breastfeeding was found to be protective, decreasing the risk of childhood-onset type 2 diabetes (odds ratio = 0.52, 95% confidence interval = 0.36-0.74). Low maternal income was significantly associated with development of childhood-onset type 2 diabetes (odds ratio = 6.67, 95% confidence interval = 3.01-14.79). CONCLUSIONS: Health and social policies and programs are needed to provide financial, educational and clinical resources that target women whose pregnancies are affected by poverty, type 2 diabetes or gestational diabetes. Breastfeeding should be encouraged to aid in the prevention of childhood-onset 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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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".