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 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 | 0.000 | 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) | 0.001 | 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".