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 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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.001 | 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".