Understanding preferences of patients with multivessel coronary artery disease towards revascularisation and optimal medical therapy: a protocol for a discrete choice experiment
Bibliographic record
Abstract
INTRODUCTION: The selection of the optimal treatment strategy remains one of the most challenging decisions in the management of coronary artery disease (CAD). Surgical and percutaneous coronary revascularisation are two widely used treatments for managing CAD and can result in improved outcomes compared with medications alone. Current practice guidelines recommend revascularisation for multivessel CAD for most patients. However, there remains uncertainty about whether revascularisation or medical therapy is optimal for managing multivessel disease for many patients, especially, in the elderly and those living with multimorbidity. Also, there is limited understanding of patient preferences towards candidate treatment options for multivessel disease. This study aims to quantify and characterise heterogeneity in patient preferences towards treatment options for multivessel CAD. METHODS AND ANALYSIS: We have designed and will administer a discrete choice experiment to elicit and quantify preferences of people with multivessel CAD towards revascularisation and optimal medical therapy for managing multivessel CAD. Multinomial logit mixed effects and hierarchical Bayes models will be used to model the association between the participants' choices and the attributes and their different levels. The relative importance of the attributes will be assessed using the size of coefficients and marginal rate of substitution (MRS), a measure of the willingness to accept a trade-off among different options. Heterogeneity in patient preferences will be evaluated using latent class analysis. ETHICS AND DISSEMINATION: Ethical approval for this study was granted by the University of Calgary Conjoint Health Research Ethics Board. Findings from this study will inform the development of clinical decision support tool that integrates patient preferences with clinical risk information to support patient-care provider discussion about optimal treatment for multivessel CAD management.
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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 | 0.076 | 0.062 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.003 |
| Research integrity | 0.007 | 0.009 |
| Insufficient payload (model declined to judge) | 0.043 | 0.008 |
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".