Update on Worldwide Trends in Occurrence of Childhood Type 1 Diabetes in 2020.
Bibliographic record
Abstract
Epidemiological data on pediatric type 1 diabetes (T1D), mainly incidence, have become increasingly available since the second half of the 20th century. Comparative incidence data across populations were only obtained since the 1980s. The 2019 IDF Atlas provides T1D incidence, prevalence and mortality estimates for children < 15 years for all 211 countries, but actual data were available for only 94 countries (only 3 low-income). The estimated prevalent cases were 600,900 and incident cases 98,200. Incidence remains highest in Finland (60/100,000/ year), Sardinia and Sweden, followed by Kuwait, some other northern European countries, Saudi Arabia, Algeria, Australia, New Zealand, USA and Canada. The lowest incidence is seen across East and South-East Asia. Globally, the average increase in incidence has been 3-4%/year over past decades, being steeper in low-incidence countries. Although T1D mortality has drastically decreased, there is still a higher risk compared with the non-diabetic population, especially in people with diabetic nephropathy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".