Provider Preferences About a Polypill for Heart Failure With Reduced Ejection Fraction: Development of a Multicenter Physician Survey Containing a Discrete Choice Experiment
Bibliographic record
Abstract
Background: Guideline-directed medical therapy (GDMT) for heart failure with reduced ejection fraction (HFrEF) reduces mortality rates but remains widely underused. A polypill for HFrEF has been proposed as an implementation strategy to improve GDMT delivery, but little is known about physicians' preferences in the design of HFrEF polypills. Discrete choice experiments (DCEs), in which survey respondents choose among hypothetical products, are a powerful tool in health economics research and can lend insight into key tradeoffs in HFrEF polypill design. However, the process of designing DCEs is complex and often poorly reported. Methods: We developed a survey instrument, including a DCE, through a 5-stage mixed-methods process including (1) literature review; (2) physician interviews and surveys; (3) attribute generation; (4) expert review; and (5) pilot testing. We applied a D-efficient design approach using a multinomial logic model to determine the number of choice tasks for the DCE. Results: We designed a DCE with 4 attributes: HFrEF polypill out-of-pocket cost, inclusion of an angiotensin converting enzyme inhibitor, angiotensin receptor blocker, or sacubitril/valsartan, ancillary support for polypill prescribing, and pharmacy availability. The final survey will be distributed to cardiologists in academic and Veterans Administration medical centers across the United States. Conclusions: Through a rigorous multistage design process leveraging mixed methods, we developed a DCE that elicits cardiologists' preferences about HFrEF polypills and key tradeoffs in their design. Results from this DCE study will directly inform future HFrEF polypill cluster-randomized clinical trials.
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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 | 0.104 | 0.082 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 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".