Exploring Patient Preferences for Adjunct-to-Insulin Therapy in Type 1 Diabetes
Bibliographic record
Abstract
OBJECTIVE While sodium–glucose cotransporter inhibitor (SGLTi) therapy has been evaluated in type 1 diabetes (T1D) trials, patient reactions to benefits and risks are unknown. Using established methodology, we evaluated patient preferences for different adjunct-to-insulin therapy options in T1D. RESEARCH DESIGN AND METHODS An online survey, completed by 701 respondents with T1D (231 U.S., 242 Canada, and 228 Germany), used conjoint analysis to present six hypothetical, masked, pairwise drug profile choices composed of different benefit-risk attributes and effect ranges. Data used in analyses were derived from actual phase 3 trials of a low-dose SGLTi (comparable to oral empagliflozin 2.5 mg q.d.), a high-dose SGLTi (comparable to oral sotagliflozin 400 mg q.d.), and an available adjunct-to-insulin therapy (comparable to subcutaneous pramlintide 60 μg t.i.d.). RESULTS Conjoint analysis identified diabetic ketoacidosis risk as most important to patients (23% relative score; z test, P < 0.05); ranked second were HbA1c reduction (14%), risk of severe hypoglycemia (13%), oral versus injectable treatment (12%), and risk of genital infection (12%). Next was risk of nausea (11%), followed by weight reduction (8%) and the risk of diarrhea (7%). A low-dose SGLTi drug profile was identified by conjoint analysis as the top patient preference (83% of participants; z test, P < 0.05) versus high-dose SGLTi (8%) or pramlintide (9%). Separate from conjoint analysis, when respondents were asked to choose their preferred adjunct-to-insulin therapy (masked to drug name/dose), 69%, 17%, 6%, and 9% of respondents chose low-dose SGLTi, high-dose SGLTi, pramlintide, and insulin therapy alone, respectively. CONCLUSIONS Low-dose SGLTi profile was the favored adjunct-to-insulin therapy by persons with T1D.
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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 | 0.008 | 0.018 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".