714-P: Assessment of the Patient Transition Experience to Hybrid Closed-Loop Insulin Pump Therapy
Bibliographic record
Abstract
Hybrid closed-loop (HCL) insulin pump therapy is a promising development for the management of type 1 diabetes (T1D), but little is known about the transition period to HCL in a real-world setting. Here, we present novel data evaluating the HCL transition experience and its effect on diabetes distress. We evaluated 32 out of 150 anticipated participants who were about to transition (Arm A), or had recently transitioned (Arm B), from standard pump therapy to the MiniMed™ 670G HCL pump. Patients completed a baseline and 6-month post-transition diabetes distress survey using the T1-DDS. In the follow-up survey, patients’ trust in HCL and treatment satisfaction were also assessed. Finally, post-survey interviews were conducted for patients to elaborate on the successes and struggles of their HCL transition experience. No significant differences were observed between the 2 arms, so the sample was analyzed as a whole. After 6 months on HCL, significant reductions were observed in overall diabetes distress (p<0.0001), as well as powerlessness (p<0.005), management (p<0.001), hypoglycemia (p<0.005), and eating (p<0.0005) distress. Most patients (56%) indicated difficulty trusting HCL, however 80% reported that trust improved over time. Patient satisfaction was high (81%), however only 47% indicated that HCL met their expectations, 56% would recommend HCL to others, and 66% indicated frustration with the transition to HCL. Patients were divided on whether HCL increased (41%) or decreased (47%) the workload required to manage their T1D (12% neutral). Over half of patients (59%) felt that HCL was too complicated. Patients were divided on whether HCL was too much of a “hassle” to use (31% yes, 38% no, 31% neutral). Emerging themes from the qualitative data include numerous alarms, loss of sleep, and necessity of “phantom carbing.” Overall, HCL therapy significantly reduces diabetes distress, but the patient transition experience is varied in terms of trust, satisfaction, and comfort with the technology. Disclosure A. Dissanayake: None. E. Chow: None. A. White: Advisory Panel; Self; Abbott Diabetes, AstraZeneca, Boehringer Ingelheim (Canada) Ltd., Eli Lilly and Company, Janssen Pharmaceuticals, Inc., Merck & Co., Inc., Novo Nordisk Canada Inc. J. E. Kapeluto: None. J. Mackenzie-feder: None. B. Schroeder: Advisory Panel; Self; AstraZeneca, Novartis Pharmaceuticals Canada Inc. M. Pawlowska: Advisory Panel; Self; Novo Nordisk.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".