Type 2 Diabetes, Medication-Induced Diabetes, and Monogenic Diabetes in Canadian Children
Bibliographic record
Abstract
OBJECTIVE: To determine in Canadian children aged <18 years the 1) incidence of type 2 diabetes, medication-induced diabetes, and monogenic diabetes; 2) clinical features of type 2 diabetes; and 3) coexisting morbidity associated with type 2 diabetes at diagnosis. RESEARCH DESIGN AND METHODS: This Canadian prospective national surveillance study involved a network of pediatricians, pediatric endocrinologists, family physicians, and adult endocrinologists. Incidence rates were calculated using Canadian Census population data. Descriptive statistics were used to illustrate demographic and clinical features. RESULTS: From a population of 7.3 million children, 345 cases of non-type 1 diabetes were reported. The observed minimum incidence rates of type 2, medication-induced, and monogenic diabetes were 1.54, 0.4, and 0.2 cases per 100,000 children aged <18 years per year, respectively. On average, children with type 2 diabetes were aged 13.7 years and 8% (19 of 227) presented before 10 years. Ethnic minorities were overrepresented, but 25% (57 of 227) of children with type 2 diabetes were Caucasian. Of children with type 2 diabetes, 95% (206 of 216) were obese and 37% (43 of 115) had at least one comorbidity at diagnosis. CONCLUSIONS: This is the first prospective national surveillance study in Canada to report the incidence of type 2 diabetes in children and also the first in the world to report the incidence of medication-induced and monogenic diabetes. Rates of type 2 diabetes were higher than expected with important regional variation. These results support recommendations that screening for comorbidity should occur at diagnosis of 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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.005 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".