Type 2 Diabetes Mellitus in Children
Bibliographic record
Abstract
BACKGROUND: Type 2 diabetes mellitus is increasingly being observed among children and youth, including the Native population of Canada. Only one study has investigated prenatal and early infancy risk factors for the disease. METHODS: A case-control study was conducted; 46 patients younger than 18 years were recruited from the only clinical center for the treatment of diabetes serving the province of Manitoba, and 92 age- and sex-matched controls were recruited from a pediatric ambulatory clinic serving a large Native population in Winnipeg, Manitoba. Information on exposure to prenatal and early infancy risk factors was obtained through questionnaires administered by a Native nurse-interviewer. RESULTS: Multiple logistic regression modeling identified preexisting diabetes (odds ratio [OR], 14.4; 95% confidence interval [CI], 2.86-72.5), gestational diabetes (OR, 4.40; 95% CI, 1.38-14.1), and breastfeeding longer than 12 months (OR, 0.24; 95% CI, 0.13-0.99) as significant independent predictors of diabetic status. Other factors, such as low (<2500 g) and high (>4000 g) birth weight and maternal obesity, were also associated with diabetes in our population, but the elevated risks were not statistically significant. CONCLUSION: Breastfeeding reduces the risk of type 2 diabetes among Native Canadian children and should be promoted as a potential intervention to control the disease.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".