Patient and physician preferences for non-invasive diagnostic cardiovascular imaging technologies: a discrete choice experiment
Bibliographic record
Abstract
PURPOSE: Diagnostic imaging techniques have to be selected for their accuracy, efficiency, cost-efficiency, and impact on outcome. But beyond that, the choice of non-invasive cardiovascular imaging tests for diagnosing coronary artery disease also has to respect patient safety and comfort. In this study, we investigated patient and physician preference in relation to the choice of cardiovascular imaging tests. RESULTS: A total of 211 subjects (148 cardiac patients and 63 physicians) were enrolled and completed a discrete choice experiment. Tests and modalities were deconstructed into 6 attributes (risks and side effects, diagnostic accuracy, patient out-of-pocket cost, type of procedure, type of scanner and test duration). A Sawtooth software choice-based conjoint analysis with hierarchical Bayes estimation was performed and showed the risks and side effects attribute was assigned the most relative importance (30%) when considering patients' preference. Patients gave notably high value to tests with milder side effects, while preferring to avoid exposure to ionizing radiation and risks associated the use of pharmacological agents inducing direct coronary arteriolar vasodilation. Physicians allocated more importance to the patient out-of-pocket cost attribute (29%). Both patients and physicians valued tests' risks and side effects, diagnostic accuracy, patient out-of-pocket cost as the three most important attributes, but in diverging order. A market simulation comparing current cardiovascular imaging tests revealed breathing maneuver-enhanced cardiac magnetic resonance had the highest shares of preference in both patients (59.6%) and physicians (32.7%). CONCLUSION: A patients' preference for a particular cardiovascular imaging test was most determined by the risks and side effects, while physicians prioritized less costly tests for their patients. In shared decision-making with patients, physicians should therefore focus on a balanced discussion of risks and side effects associated with cardiovascular imaging tests. Both, patients and physicians would prefer a cardiovascular MR imaging test using a vasoactive breathing maneuver instead of currently used alternatives that require intravenous contrast agents, pharmacological stress, or radiation.
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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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".