1298-P: “I Would Rather Bolus”: Youth and Parents Prefer Manual Bolusing to Carbohydrate Limitations in a Fully Automated Artificial Pancreas (AP) System
Notice bibliographique
Résumé
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)
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,002 | 0,005 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,001 | 0,001 |
| Science ouverte | 0,000 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,004 | 0,001 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule source (Gemma direct ou Codex distillé), pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».