132-OR: Ten-Year Evaluation of Diabetes Categories in Youth Shows a Continuous Increase in Frequency of Newly Diagnosed Type 2 Diabetes—Results from the Worldwide SWEET Registry
Bibliographic record
Abstract
Background: To evaluate the frequency of youth with type 2 diabetes (T2D) using data from the multinational consortium SWEET e.V. Methods: The frequency of newly diagnosed T2D across all categories of diabetes in youth under 21 registered in the SWEET database during 2012-2021 was analysed. Trends in biennial proportions in five world regions (Europe, EU; Australia/New Zealand, AU/NZ; South America, SA; North America/Canada, NA; Asia/Africa, AS/AF) were estimated using logistic regression models adjusted for age at onset and sex. Results: Table 1 shows the proportion of patients across diabetes categories over the study period in two-year steps. The increase in T2D rate was 9.0% per two years [95% CI 5.7-12.3] and was significant in EU (10.4% [3.8-17 .3]), AU/NZ (13.6% [3.5-24.7]), and NA (8.7% [3.8-14.0]). The overall increase in T2D during COVID-19 pandemic was similar to the previous biennial increase, while AU/NZ and NA showed the highest increase (respectively from 9.5% to 12.2%, p=0.999; from 7.7% to 13.2%, p<0.001). Conclusions: There has been a steady increase in T2D observed worldwide over time. Findings suggest that more prevention efforts are needed to contain the public health impact in the near future. The Covid-19 pandemic did not affect the overall trend in frequency of youth with T2D observed over the 10-year period. Disclosure V.Cherubini: None. S.Shah: Research Support; Boehringer-Ingelheim, Takeda Pharmaceutical Co., Ltd. A.Vamvakis: None. R.Gesuita: None. On behalf of the sweet study group: n/a. A.J.Eckert: None. S.Amed: None. S.Besançon: None. F.Cavallo aita: None. N.A.Crimmins: None. E.F.Gevers: Other Relationship; Novo Nordisk, Speaker's Bureau; Novo Nordisk. C.A.Jefferies: None. J.Kim: None.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".