Patient preferences for newer oral therapies in type 2 diabetes
Bibliographic record
Abstract
BACKGROUND: We aimed to evaluate patient preferences towards three oral antihyperglycaemic therapies using conjoint analysis to determine which attributes may influence use. METHODS: We used an online survey, completed by 553 US respondents with type 2 diabetes mellitus (T2DM; mean age 64 ± 9 years; 55% had cardiovascular [CV] risk; 27% had CV disease), to present hypothetical, blinded, pairwise, drug profile comparison choices, between different benefit/risk attributes and effect ranges. Attributes were derived from phase 3 trials for empagliflozin 25 mg (SGLT2 inhibitor), oral semaglutide 14 mg (GLP-1 receptor agonist) and sitagliptin 100 mg (DPP-4 inhibitor). Predicted therapy preference outcomes and relative importance of each attribute were calculated (presented as a percentage). RESULTS: Preference score was highest for the profile matching empagliflozin (56%), versus sitagliptin (38%; z-test, P < 0.001) and oral semaglutide (6%, z-test, P < 0.001). Results were overall consistent in subgroup analyses. Genital infection risk was the most important attribute (relative score: 19% [z-test, P = 0.077]). Other important attributes were fasting requirements (15%), weight reduction (15%), risk of vomiting (14%), CV benefit (12%), and risk of nausea (11%). HbA1c reduction (8%) and ability to take medication with other drugs (6%) were considered less important. While blinded to drug name/dose, respondents chose a drug profile similar to empagliflozin (41%) versus sitagliptin (31%), oral semaglutide (11%), or 'none of the options' (17%). CONCLUSION: While the drug profile comparable to empagliflozin was preferred, CV benefit was not the top patient priority. A shared physician-patient decision model and increased patient education are needed to ensure optimal use of guideline-directed T2DM therapies.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".