179-LB: Characteristics of Children with Medication-Induced Diabetes (MID): A Canadian Pediatric Surveillance Program (CPSP) Study
Bibliographic record
Abstract
Objectives: There is limited data on MID in children. This study aimed to compare the demographic and clinical characteristics of 1) a historical MID cohort to a new cohort and 2) children with MID based on overweight/obese BMI (O-BMI) and normal weight BMI (N-BMI). Methods: National surveillance was conducted via the CPSP. Pediatric clinicians reported new cases (children <18 years old) of non-type 1 diabetes for 24-months (cohort 1: 2006-08, cohort 2: 2017-19). MID cases were classified by the presence of diabetes as defined by Diabetes Canada and exposure to a diabetogenic medication. Comparisons were made between the two cohorts and between MID patients with O-BMI (BMI ≥85th percentile for age and sex) and N-BMI (BMI <85th percentile for age and sex). Descriptive statistics and Fisher exact, chi-squared and t-tests were used where applicable. Results: There were 55 cases of MID in cohort 1 and 51 cases in cohort 2, with no significant differences between cohorts. Mean age (+/- SD) at presentation was 13.1 +/- 3.6 years and 13.3 +/- 3.5 years in cohort 1 and 2, respectively. Glucocorticoid therapy was documented in >95% of cases in both cohorts. In the combined cohorts there were 50 cases with N-BMI and 43 cases with O-BMI. There were no statistically significant differences in age, sex or ethnicity across BMI groups. Mean hemoglobin A1c (+/- SD) (n = 40) was 6.45% +/- 1.11% in the N-BMI group and 7.26% +/- 2.51% in the O-BMI group (p = 0.184). The O-BMI group were more likely to have a parent with type 2 diabetes (T2D) (p = 0.042) and acanthosis nigricans (p = 0.007) compared to the N-BMI group. Conclusions: The demographic and clinical features of children with MID have not changed over a 10-year period. The presence of acanthosis nigricans and/or a parent with T2D may be used to identify children who require more intensive glycemic monitoring when on a diabetogenic medication. Further studies are needed to better understand the evolution of MID and the risk it confers to developing T2D. Disclosure T.J. Patel: None. A. Ayub: None. M.A. Irvine: None. S. Hadjiyannakis: None. M. Henderson: None. M.A. Nour: None. T. Pinto: None. B. Wicklow: None. J. Hamilton: None. E. Sellers: None. S. Amed: None. Funding Diabetes Canada; Manitoba Institute for Child Health and Sick Kids Hospital; Public Health Agency of Canada (F18-02353)
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".