Predischarge exercise electrocardiogram and stress echocardiography can predict long‐term clinically driven revascularization following acute myocardial infarction
Bibliographic record
Abstract
BACKGROUND: Predischarge stress testing provides suboptimal prediction of spontaneous hard events following uncomplicated acute myocardial infarction (AMI). HYPOTHESIS: This study was aimed at assessing whether soft cardiac ischemic events requiring late revascularization could be predicted more accurately. METHODS: In all, 428 patients undergoing exercise electrocardiography (ECG) and stress echocardiography (SE, 345 dobutamine and 83 dypiridamole) within 15 days of uncomplicated AMI were followed up for 425 (range 20-2220) days. Soft ischemic events (effort angina>class II [Canadian Cardiovascular Society Classification] and unstable angina) driving late (>6 months) revascularization were regarded as endpoints. RESULTS: A total of 58 events (29 effort and 29 unstable angina with subsequent 47 coronary artery bypass grafts and 11 percutaneous transluminal coronary angioplasties) occurred: 26 in patients with positive exercise ECG and 34 in patients with positive SE. Univariate predictors of revascularizations were positive exercise ECG (p = 0.0001), peak wall motion score index (WMSI) (p = 0.0009), low workload (p = 0.0018), rest WMSI (p = 0.02) and positive SE (p = 0.02). Cox multivariate analysis selected peak WMSI, positive exercise ECG, and low workload positive exercise ECG as independent predictors of late revascularizations. CONCLUSIONS: Predischarge stress testing identifies the long-term occurrence of soft ischemic events driving late revascularization after uncomplicated AMI.
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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 | 0.002 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".