942-P: Pediatric and Adult T1D Glycemic, QoL, and Safety Outcomes of 1,013 Single Clinic Open Source AID Installations
Bibliographic record
Abstract
Introduction and Objective: Since 2020, BCDiabetes, a public Canadian clinic, has offered clients open source automated insulin delivery (OS AID) & ongoing support at no cost due to OS AID’s lower barrier to entry vs retail. Methods: This retrospective study analyzed client data from 14-day CGM ambulatory glucose profiles before and during use of OS AID. Clients had T1D, used Omnipod pods & Dexcom CGM, and signed a consent to use OS AID via smartphone app: Loop, iAPS or AAPS. Results: 1588 clients had OS AID installed for 1730 patient-years of exposure. 33% were pediatric & 47% were male. For adult & pediatric clients respectively, average age (range) was 42 (18-84) and 12 (2-17) years. T1D duration was 21 years for adults and 3 years for peds. Loop, iAPS & AAPS were used by 75, 10 & 15% respectively. No deaths, 2 DKAs and 8 severe hypoglycemic episodes were reported. Glycemic outcomes for 740 adult and 273 pediatric clients with paired baseline & 90 day CGM data were examined (Table 1). Outcomes by app (Loop, iAPS, AAPS) were near-identical so are pooled here. Quality of Life: 194 adult & 61 pediatric clients had paired QoL data pre- & 90 days post-OS AID. Diabetes Distress fell from 2.47 to 1.97 (p<0.0001), and Device Satisfaction rose from 6.38 to 8.2 (p<0.0001). Conclusion: OS AID systems, when installed and supported by clinicians, were safe, improved glycemia, lowered diabetes distress and improved device satisfaction. Disclosure A. Alqahtani: None. N. Khan: None. G. Klein: None. K. Hawke: None. T. Elliott: None.
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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 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".