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 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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".