Open-Source Versus Commercial Automated Insulin Delivery System for Type 1 Diabetes Management: A Prospective Observational Comparative Study from Canada
Bibliographic record
Abstract
Objective:This study compares unregulated open-source (OS) automated insulin delivery (AID) systems and commercial-AID (C-AID) systems regarding glucose management, patient-reported outcomes (PROs), and safety among adults with type 1 diabetes (T1D). Methods:We conducted a 12-week, prospective, observational, noninferiority, comparative, real-world study involving 78 adults with T1D and having used an AID system for ≥3 months (26 OS-AID and 52 C-AID users). A total of 4-week data from a blinded continuous glucose monitor was used to assess the effectiveness in glucose management (primary outcome: 24 h time in range [TIR%] for 4 weeks, with a noninferiority margin of 5%). Results:Our study suggested that OS-AIDs were noninferior to C-AIDs regarding the 24 h TIR% (78.3% [standard deviation or SD 11.0] vs. 71.2% [SD 10.9], mean difference 7.2% [95.08% confidence interval or CI: 1.9% to 12.5%], P < 0.001), even after adjusting for various confounding factors. OS-AIDs spent more time in hypoglycemia (<3.9 mmol/L) than C-AIDs (3.9% [SD 3.1] vs. 1.8% [SD 1.3], P < 0.001) yet within the recommended range. OS-AID users reported less fear of hypoglycemia, while other PRO measures (diabetes distress, hypoglycemia awareness, sleep, fear of hypoglycemia, treatment satisfaction, and overall quality of life) were not different between groups. No severe hypoglycemia or diabetic ketoacidosis was reported in either group, with a similar occurrence rate of technical issues during the 12-week study period. Conclusions:OS-AIDs are safe and noninferior to C-AIDs for TIR% among adults with T1D in real-world settings. Both OS-AID and C-AID systems can be considered for T1D management.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".