Prediction of 1-Year Survival After Thrombolysis for Acute Myocardial Infarction in the Global Utilization of Streptokinase and TPA for Occluded Coronary Arteries Trial
Bibliographic record
Abstract
BACKGROUND: When a patient survives thrombolysis for acute myocardial infarction, little information from large studies exists from which to estimate prognosis during follow-up visits. METHODS AND RESULTS: Baseline, in-hospital, and later survival data were collected from 41 021 patients enrolled in Global Utilization of Streptokinase and TPA for Occluded Coronary Arteries, a randomized trial of 4 thrombolytic-heparin regimens with standard aspirin and beta-blockade. Cox proportional hazards models were developed to predict 1-year survival in 30-day survivors (n=37 869) from baseline clinical and ECG factors and in-hospital factors; a combined model then was developed (C-index 0.800). The model was simplified into a nomogram to predict individual outcomes (C-index 0.754). Factors reflecting demographics (advanced age, lighter weight), larger infarctions (higher Killip class, lower blood pressure, faster heart rate, longer QRS duration), cardiac risk (smoking, hypertension, prior cerebrovascular disease), and arrhythmia were important predictors of death between 30 days and 1 year. Black race was associated with a substantial increase in risk after considering other factors. Revascularization was associated with reduced risk between 30 days and 1 year. CONCLUSIONS: When evaluating a patient who has survived acute infarction treated with thrombolysis, clinicians can estimate the likelihood of survival from factors easily measured during admission. Although many risk factors clearly relate to age, left ventricular dysfunction, or clinical instability, black race is an unexplained risk factor requiring further examination.
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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 | 0.004 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".