1298-P: “I Would Rather Bolus”: Youth and Parents Prefer Manual Bolusing to Carbohydrate Limitations in a Fully Automated Artificial Pancreas (AP) System
Bibliographic record
Abstract
Aim: Although hybrid closed loop insulin delivery systems simplify self-care with automated insulin delivery, it remains necessary for patients to enter planned carbohydrate intake for meals and snacks. We interviewed children, teens, and young adults with T1D and parents of youth with T1D about their willingness to trade off limiting carb intake to 50g if this would remove need to manually bolus for each meal/snack. Methods: Semi-structured interviews were conducted with 39 youth, ages 10-25 years, and 44 parents of youth at 2 U.S. diabetes centers. Interviews were audio-recorded, transcribed, and coded using thematic analysis. Youth (72% female, 82% white) were (M±SD) age 17.0±4.7 years, with T1D duration 9.4±4.9 years and A1c 8.4±1.1%; 79% were pump users and 82% were CGM users. Of parents, 86% were white and 91% were mothers. Results: Most youth and parents strongly preferred to manually bolus for meals/snacks rather than use a fully automated system that requires limiting carb intake at each meal/snack; many stated they did not want to feel restricted. However, both youth and parents said they would like automatic coverage for meals/snacks of <50g if they also had the option to bolus for higher carb intake. The majority believed 50g was too little for meals and some suggested a higher allowable carb intake would be acceptable to them. The few participants who were willing to limit carb intake tended to eat <50g of carbs at meals already. Youth reported that any automation without carb limitation would make self-care easier; parents reported that automation without carb limitation would reduce overall mental burden. Conclusions: Youth and parents agreed that a fully automated system that did not require manual bolusing would reduce physical and mental burdens of care, but not if it limited carb intake in order for the system to work effectively. AP designers should address patient and parent aversions to dietary restrictions in future AP devices. 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. Research Support; Self; Dexcom, Inc., DreaMed Diabetes, Tandem Diabetes Care, Xeris Pharmaceuticals, Inc. Speaker’s Bureau; Self; Roche Diabetes Care. Other Relationship; Self; Dexcom, Inc., Insulet Corporation, Roche Diabetes Care. S.A. Weinzimer: Consultant; Spouse/Partner; Tandem Diabetes Care. Consultant; Self; Zealand Pharma A/S. Speaker’s Bureau; Self; Insulet Corporation. L.M. Laffel: Advisory Panel; Self; Roche Diabetes Care. Consultant; Self; Boehringer Ingelheim Pharmaceuticals, Inc., ConvaTec Inc., Dexcom, Inc., Insulet Corporation, Insulogic LLC, Janssen Pharmaceuticals, Inc., Lilly Diabetes, Novo Nordisk Inc., Sanofi US. Funding National Institutes of Health (P30DK036836, DP3DK113511, DP3DK104057, K12DK094721, T32DK007260)
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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 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".