The HEART score in the era of the European Society of Cardiology 0/1-hour algorithm
Bibliographic record
Abstract
Background: The European Society of Cardiology’s 0/1-hour algorithm improves the early triage of patients towards “rule-out” or “rule-in” of non-ST-segment elevation myocardial infarction. The HEART score is a risk stratification tool for patients with undifferentiated chest pain. We sought to evaluate the performance of the European Society of Cardiology 0/1-hour algorithm and the HEART score to evaluate chest pain patients in the emergency department. Methods: In this prospective study, we applied the European Society of Cardiology 0/1-hour algorithm and the HEART score in 1355 consecutive patients who presented to the emergency department with symptoms suggestive of acute coronary syndrome without ST-segment elevation. Patients were followed for non-ST-segment elevation myocardial infarctions and major adverse cardiac events at 30 days: death, non-ST-segment elevation myocardial infarction, or unplanned coronary revascularization. Results: The European Society of Cardiology 0/1-hour algorithm classified 921 (68.0%) patients as “rule-out” and the HEART score classified 686 (50.6%) patients as “low-risk”. The 30-day incidence of non-ST-segment elevation myocardial infarctions was 0.32% in the European Society of Cardiology 0/1-hour algorithm “rule-out” patients versus 0.29% in the HEART score “low-risk” patients ( p=0.75). The rate of major adverse cardiac events was 7.7% in the European Society of Cardiology 0/1-hour algorithm “rule-out” patients versus 1.1% in the HEART score “low-risk” patients ( p<0.001). Conclusion: The European Society of Cardiology 0/1-hour algorithm identified more patients with low risk of non-ST-segment elevation myocardial infarctions at 30 days whereas for major adverse cardiac events, the HEART score had a greater capacity to detect low-risk patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.008 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.004 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".