Incidence Trends of Type 2 Diabetes Mellitus, Medication-Induced Diabetes, and Monogenic Diabetes in Canadian Children, Then (2006–2008) and Now (2017–2019)
Bibliographic record
Abstract
Introduction. The landscape of childhood diabetes has evolved and addressing the knowledge gaps in non-Type 1 diabetes mellitus are key to accurate diagnosis. Objectives. A national surveillance study was completed between 2006 and 2008 and then repeated between 2017 and 2019 to describe Canadian incidence trends and clinical characteristics of non-Type 1 diabetes mellitus. Methods. We prospectively tracked new cases of non-Type 1 diabetes mellitus in children <18 years of age between June 1, 2017 and May 31, 2019. For each reported new case, a detailed questionnaire was completed, and cases were classified as Type 2 diabetes mellitus, medication-induced diabetes (MID), monogenic diabetes, or “indeterminate.” Minimum incidence rates and 10-year incidence trends of non-Type 1 diabetes mellitus and its subtypes were calculated. Results. 441 cases of non-Type 1 diabetes mellitus were included (Type 2 diabetes mellitus = 332; MID = 52; monogenic diabetes = 30; indeterminate = 27). Compared to 10 years ago, the incidence of MID and monogenic diabetes remained stable, while Type 2 diabetes mellitus increased by 60% ( p < 0.001 ) overall and by 37% ( p = 0.005 ) and 50% ( p = 0.001 ) in females and males, respectively. Type 2 diabetes mellitus incidence increased by 1.5 times in Indigenous ( p < 0.001 ) and doubled in Asian ( p = 0.003 ) children. Conclusions. Canadian incidence rates of childhood-onset Type 2 diabetes mellitus have significantly increased. Further research, policy, and prevention efforts are needed to curb rising rates of youth onset Type 2 diabetes mellitus.
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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 | 0.003 | 0.006 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".