Abstract 9041: Provider Evaluation for Selection of Evidence-Based Therapies to Reduce Cardiovascular Complications in Patients With Type 2 Diabetes Mellitus Study - Primary Results of the PREFER-DM Study
Bibliographic record
Abstract
Introduction: The attributes that health care providers (HCPs) consider in selecting oral antihyperglycemic agents in the treatment of diabetes are not well understood. Methods: In the PREFER-DM study, we used conjoint analysis to evaluate the drug attributes preferred by HCPs toward 3 oral antihyperglycemic drugs: empagliflozin 25 mg (SGLT2i), oral semaglutide 14 mg (GLP-1 RA) and sitagliptin 100 mg (DPP-4 inhibitor). HCPs in the US who regularly treat patients with T2DM were sent an online survey via email; those who participated were asked to select from 6 hypothetical, blinded pair-wise drug profile comparisons of different drug benefits and side effects/risk attributes. Attributes in the survey were derived from published trials and FDA labeling data. The relative importance of one attribute relative to another was calculated. Results: The PREFER-DM survey was distributed to 12,806 HCPs and 1052 valid surveys were completed (365 [35%] primary care physicians, 350 [33%] endocrinologists, 337 [32%] cardiologists). Risk reduction of cardiovascular death was the most important perceived attribute (z-test P <0.05; Figure). Attributes of reduction in heart failure, risk of nausea/vomiting, decreasing progression of renal disease, and weight reduction were considered to have similar importance. The attributes least selected by HCPs were food and medication restriction and A1c lowering (both had similar importance) followed by risk of diabetic ketoacidosis, risk of genital infection, and avoidance of severe hypoglycemia. The relative ordering of attributes remained consistent across specialties (Figure). Conclusion: HCPs across specialities prefer oral antihyperglycemic drug attributes associated with CV risk reduction over side effect profile, tolerability, and adverse outcomes. Whether these preferences translate to prescription patterns and patient acceptance of oral antihyperglycemic drugs in clinical settings remains to be investigated.
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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 | 0.011 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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) | 0.004 | 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".