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 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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".