1361-P: Features to Increase Glycemic Benefits in an Ideal Artificial Pancreas (AP): Perspectives of Young Persons with Type 1 Diabetes (T1D)
Bibliographic record
Abstract
Aim: Patient expectations for an ideal AP system demand creative and attractive solutions in order to maintain engagement and positively impact glucose levels in young persons with T1D. To explore pediatric opinions on optimizing glycemic benefits of AP systems, we interviewed children, teens, and young adults with T1D experienced with diabetes technologies. Methods: Semi-structured interviews were conducted at two diabetes centers in 39 youth with T1D, ages 10-25 years and T1D duration for ≥1 year. Interview transcripts were coded and reviewed using thematic analysis. Participants (72% female, 82% white) had mean±SD age of 17.0±4.7 years, T1D duration 9.4±4.9 years, and A1c 8.4±1.1%; 79% were pump-treated and 82% were CGM users. Results: Participants most often suggested an ideal system would improve glucose and A1c by: 1) recognizing and managing glucose trends related to food and exercise; 2) adjusting insulin doses for glucose variations and/or incorporating a mitigation for hypoglycemia (glucagon, glucose, etc.); and 3) providing conveniences to maintain engagement (easy to use, smaller sites/devices, better adhesives, longer-lasting infusion sets/sites, remote monitoring from an app, mobile phone, etc.). While most participants preferred a fully automated system, many preferred to continue manual meal-time bolusing in order to avoid any limits on carbohydrate intake. Other aspects of an ideal system included adjustments for alcohol, auto-insertions, automatic upgrades, increased battery life, personalized algorithms, and the ability to override the system due to concerns about trust. Conclusion: Stakeholders should be encouraged that young persons with T1D recognize potential glycemic benefits of AP systems, even without full automation. Understanding and incorporating the features preferred by young persons with T1D into the designs of AP systems will be necessary to maximize uptake and maintain durable AP use. Disclosure P.V. Commissariat: None. L. Roethke: None. J.L. Finnegan: None. L.K. Volkening: None. D.E. McGill: None. E. Dassau: Consultant; Self; Eli Lilly and Company, Insulet Corporation. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Ltd., Insulet Corporation, Roche Diabetes Care, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker's Bureau; Self; Roche Diabetes Care. Other Relationship; Self; ModAGC. S.A. Weinzimer: Consultant; Self; Eli Lilly and Company, Sanofi. Consultant; Spouse/Partner; Tandem Diabetes Care. Consultant; Self; Zealand Pharma A/S. Speaker's Bureau; Self; Insulet Corporation, Medtronic MiniMed, Inc., Tandem Diabetes Care. Stock/Shareholder; Self; InsuLine Medical Ltd. L.M. Laffel: Advisory Panel; Self; Lilly Diabetes, Novo Nordisk A/S, Roche Diabetes Care, Sanofi. Consultant; Self; AstraZeneca, Boehringer Ingelheim International GmbH, Dexcom, Inc., Janssen Pharmaceuticals, Inc., UpToDate. Funding National Institutes of Health (DP3DK113511, DP3DK104057, T32DK007260, P30DK036836, K12DK094721)
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.000 | 0.002 |
| 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".