Can Symptom Presentation Predict Unstable Angina/Non-ST-Segment Elevation Myocardial Infarction in a Moderate-Risk Cohort?
Bibliographic record
Abstract
BACKGROUND: Accurate recognition of acute coronary syndromes (ACS) on initial presentation is key to minimizing morbidity and mortality. The wide spectrum of symptom presentation in ACS complicates recognition. Unstable angina/non-ST-elevation myocardial infarction (UA/NSTEMI) may be particularly difficult to diagnose as patients often do not exhibit initial high-risk features, leaving the clinician with symptom presentation alone, on which to base decisions regarding further investigation and treatment. PURPOSE: The aim of this study was to compare typical symptom presentation (classic description of angina) and atypical presentation in a cohort presenting with symptoms suggestive of UA/NSTEMI. METHOD: A prospective cohort design was used to evaluate 100 patients enrolled in an Emergency Department Chest Pain Program. RESULTS: Although patients with typical presentation were more likely to have UA/NSTEMI, atypical presentation did not rule out this diagnosis. Of the 31 patients with UA/NSTEMI, most (n=23, 74.2%) had atypical symptoms. Male gender, symptom location, and history of ischemic heart disease were significantly associated with UA/NSTEMI. Of those with a final diagnosis of UA/NSTEMI, there was no difference in symptom presentation based on age or gender. CONCLUSION: Clinicians should not rely on classic descriptions of angina when evaluating patients suspected of UA/NSTEMI.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".