The SWEET Project 10-Year Benchmarking in 19 Countries Worldwide Is Associated with Improved HbA1c and Increased Use of Diabetes Technology in Youth with Type 1 Diabetes
Bibliographic record
Abstract
Objective: The international SWEET registry (NCT04427189) was initiated in 2008 to improve outcomes in pediatric diabetes. A 10-year follow-up allowed studying time trends of key quality indicators in 22 centers from Europe, Australia, Canada, and India in youth with type 1 diabetes (T1D). Methods: Aggregated data per person with T1D <25 years of age were compared between 2008–2010 and 2016–2018. Hierarchic linear and logistic regression models were applied. Models were adjusted for gender, age-, and diabetes duration groups. Results: The first and second time periods included 4930 versus 13,654 persons, 51% versus 52% male, median age 11.3 [Q1; Q3: 7.9; 14.5] versus 13.3 [9.7; 16.4] years, and T1D duration 2.9 [0.8; 6.4] versus 4.2 [1.4; 7.7] years. The adjusted hemoglobin A1C (HbA1c) improved from 68 (95% confidence interval [CI]: 66–70) to 63 (60; 65) mmol/mol ( P < 0.0001) or 8.4 (95% CI: 8.2–8.6) to 7.9 (7.6; 8.1) % ( P < 0.0001). Across all age groups, HbA1c was significantly lower in pump and sensor users. Severe hypoglycemia declined from 3.8% (2.9; 5.0) to 2.4% (1.9; 3.1) ( P < 0.0001), whereas diabetic ketoacidosis events increased significantly with injection therapy only. Body mass index-standard deviation score also showed significant improvements 0.55 (0.46; 0.64) versus 0.42 (0.33; 0.51) ( P < 0.0001). Over time, the increase in pump use from 34% to 44% preceded the increase in HbA1c target achievement (<53 mmol/mol) from 21% to 34%. Conclusions: Twice yearly benchmarking within the SWEET registry was associated with significantly improved HbA1c on a background of increasing pump and sensor use for 10 years in young persons with T1D. Trial Registration: NCT04427189.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.008 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.001 | 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".