Clinical decision-making of cardiologists regarding admission and treatment of patients with suspected unstable angina or non-ST-elevation myocardial infarction: protocol of a clinical vignette study
Bibliographic record
Abstract
INTRODUCTION: Cardiologists face the difficult task of rapidly distinguishing cardiac-related chest pain from other conditions, and to thoroughly consider whether invasive diagnostic procedures or treatments are indicated. The use of cardiac risk-scoring instruments has been recommended in international cardiac guidelines. However, it is unknown to what degree cardiac risk scores and other clinical information influence cardiologists' decision-making. This paper describes the development of a binary choice experiment using realistic descriptions of clinical cases. The study aims to determine the importance cardiologists put on different types of clinical information, including cardiac risk scores, when deciding on the management of patients with suspected unstable angina or non-ST-elevation myocardial infarction. METHODS AND ANALYSIS: Cardiologists were asked, in a nationwide survey, to weigh different clinical factors in decision-making regarding patient admission and treatment using realistic descriptions of patients in which specific characteristics are varied in a systematic way (eg, web-based clinical vignettes). These vignettes represent patients with suspected unstable angina or non-ST-elevation myocardial infarction. Associations between several clinical characteristics, with cardiologists' management decisions, will be analysed using generalised linear mixed models. ETHICS AND DISSEMINATION: The study has received ethics approval and informed consent will be obtained from all participating cardiologists. The results of the study will provide insight into the relative importance of cardiac risk scores and other clinical information in cardiac decision-making. Further, the results indicate cardiologists' adherence to the European Society of Cardiology guideline recommendations. In addition, the detailed description of the method of vignette development applied in this study could assist other researchers or clinicians in creating future choice experiments.
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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 | 0.039 | 0.054 |
| Meta-epidemiology (narrow) | 0.002 | 0.003 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.006 | 0.003 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.005 | 0.007 |
| Insufficient payload (model declined to judge) | 0.025 | 0.004 |
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".