The impact of the HEART score on the prevalence of cardiac testing and patient outcomes in a rural emergency department
Bibliographic record
Abstract
INTRODUCTION: This study was conducted to examine the use of the HEART score for risk stratification of chest pain patients presenting to rural Ontario emergency departments (EDs), assessing both its validity in a rural context and its utility in health-care resource management. METHODS: This study was a retrospective chart review of adult patients presenting to the ED with chest pain. The HEART score was assessed for its ability to risk-stratify patients (high, moderate and low) in terms of the likelihood of a major adverse cardiac event (MACE) within 6 weeks. The prevalence of follow-up testing for each risk category of patients was then determined such that the potential impact on health resource management was estimated based on the number of tests ordered in low-risk patients. RESULTS: Of the 215 charts included, 24 (11.2%) patients experienced a MACE within 6 weeks. None of the patients with a low HEART score experienced a MACE. In comparison, the incidence of MACE in moderate- and high-risk groups was calculated to be 13.9% (95% confidence interval [CI] [5.91% and 21.89%, respectively]) and 66.7% (95% CI [46.54% and 86.86%, respectively]). Eighteen percent of the low-risk patients received follow-up testing with no positive results suggestive of acute coronary syndrome. CONCLUSION: Our results provide external validation of the predictive value of the HEART score in determining the risk of MACE in patients presenting to a rural ED with chest pain. Our results also suggest that rates of follow-up testing in low-risk patients may be reduced in communities with limited access to resources.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".