900-P: Patient-Reported Outcomes of Adults with Type 1 Diabetes Using Do-It-Yourself Compared with Commercial Automated Insulin Delivery Systems
Bibliographic record
Abstract
Introduction: We aim to assess the difference between open-source do-it-yourself (DIY) and commercial automated insulin delivery (AID) systems in patient-reported outcomes (PRO) of adults with type 1 diabetes (T1D). Methods: Analysis from a prospective, non-inferiority, non-randomized, parallel-cohort study involving 78 non-pregnant adults with T1D, AID users ≥3 months and living in Canada. Participants (25 DIYAID and 53 commercial AID users, 60.3% females, mean age 41.2±14.6 years, mean T1D duration 27.0±14.7 years, median [Q1, Q3] duration of AID use 15.6 [7.8, 27, 4] months, mean HbA1c 6.7±0.7%, median time in range [TIR 70-180 mg/dl] 74.3% [66.8, 81.0]) completed the following validated PRO questionnaires: Diabetes Distress Scale (DDS, high distress level defined as >2.0), Diabetes Treatment Satisfaction Questionnaire (DTS-Q), An Audit of Diabetes-Dependent Quality of Life (ADD-QoL), Hypoglycemia Fear Score II (HFS-II, fear of hypoglycemia defined as scoring ≥3 in any worry subscale item), Pittsburgh Sleep Quality Index (PSQI, poor sleep defined as >5), Clarke and Gold score (hypoglycemia unawareness defined as either score ≥4). Results: DIYAID users reported better sleep quality (66.7% vs 33.3%, p=0.02) and lower HFS-II worry subscale score (28.2 vs 33.2, p=0.03) than commercial AID users, but results did not remain significant after adjusting for sex, age, level of education, T1D and AID use duration, HbA1c, and TIR. The two groups were otherwise comparable in other PROs. Overall, one third of participants reported diabetes distress, 88.3% reported poor sleep, 20.8% reported impaired hypoglycemia awareness and 80.5% reported fear of hypoglycemia. Conclusion: Similar broad QoL and other PROs were observed between DIYAID and commercial AID users, with a persistent and significant diabetes burden for all, despite advanced diabetes technology use and optimal glycemic management. Disclosure M.Lebbar: None. Z.Wu: Other Relationship; Eli Lilly and Company. A.C.Bonhoure: Consultant; Dexcom, Inc. V.Messier: None. A.Brazeau: Other Relationship; Dexcom, Inc., Diabète québec, Ordre des diététistes nutritionnistes du Québec, Research Support; Canadian Institutes of Health Research, Fonds de recherche du Québec en Santé. R.Rabasa-lhoret: Consultant; Dexcom, Inc., Abbott, Janssen Pharmaceuticals, Inc., Novo Nordisk Canada Inc., Sanofi, Lilly, Tandem Diabetes Care, Inc., Insulet Corporation. Funding Canadian Institutes of Health Research (148464)
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".