<i>Worldwide differences in childhood type 1 diabetes: The</i> SWEET <i>experience</i>
Bibliographic record
Abstract
OBJECTIVE: To study worldwide differences in childhood diabetes, comparing relevant indicators among five regions within the SWEET initiative. SUBJECTS: We investigated 26 726 individuals with type 1 diabetes (T1D) from 54 centers in the European region; 7768 individuals from 30 centers in the Asia/Middle East/Africa region; 2642 people from five centers in Australia/New Zealand; 10 839 individuals from seven centers in North America, and 1114 patients from five centers in South America. METHODS: The SWEET database was analyzed based on the following inclusion criteria: T1D, time period 2015-2019, and age < 21 years, with analysis of the most recent documented year of therapy. For the statistical analysis, we used multivariable linear and logistic regression models to adjust for age (<6 years, 6- < 12 years, 12- < 18 years, 18- < 21 years), gender, and duration of diabetes (<2 years, 2- < 5 years, 5- < 10 years, ≥10 years). RESULTS: Adjusted HbA1c means ranged from 7.8% (95%-confidence interval: 7.6-8.1) in Europe to 9.5% (9.2-9.8) in Asia/Middle East/Africa. Mean daily insulin dose ranged from 0.8 units/kg in Europe (0.7-0.8) and Australia/New Zealand (0.6-0.9) to 1.0 unit/kg 0.9-1.1) in Asia/Middle East/Africa. Percentage of pump use was highest in North America (80.7% [79.8-81.6]) and lowest in South America (4.2% [3.2-5.6]). Significant differences between the five regions were also observed with regards to body mass index SD scores, frequency of blood glucose monitoring and presence of severe hypoglycaemia. CONCLUSIONS: We found significant heterogeneity in diabetes care and outcomes across the five regions. The aim of optimal care for each child remains a challenge.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".