Real-World Use of Do-It-Yourself Artificial Pancreas Systems in Children and Adolescents With Type 1 Diabetes: Online Survey and Analysis of Self-Reported Clinical Outcomes
Bibliographic record
Abstract
BACKGROUND Patient-driven initiatives have made uptake of Do-it-Yourself Artificial Pancreas Systems (DIYAPS) increasingly popular amongst people with diabetes of all ages. Observational studies show improvements in glycemic control and quality of life amongst adults. However, there remains a lack of research examining experiences of children and adolescents with DIYAPS in everyday life and their social context. OBJECTIVE This study assesses self-reported clinical outcomes of the pediatric population using DIYAPS in real-world use. METHODS An online survey was distributed to caregivers, assessing HbA1c and Time in Range (TIR) before and after, and problems during DIYAPS use. RESULTS 209 caregivers from 21 countries responded. Children were 47.4% female with a median age of 10y; a mean diabetes duration of 5.1y ±3.9 and 10.3m ±10.0 experience with DIYAPS. Clinical outcomes improved significantly, including HbA1c (from 6.91% ±0.88 to 6.27% ±0.67; P<.001) and TIR (from 64.2% ±15.94 to 80.68% ±9.26; P<.001). CONCLUSIONS Improved glycemic outcomes were found across all pediatric age groups including adolescents and very young children. These findings are in line with clinical trial results from commercially developed closed-loop systems. CLINICALTRIAL The study was approved by the Charité ethics committee (EA2/140/18).
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".