Reperfusion Strategies in Acute Coronary Syndromes
Bibliographic record
Abstract
The appropriate timing of angiography to facilitate revascularization is essential to optimize outcomes in patents with ST-segment-elevation myocardial infarction and non-ST-segment-elevation acute coronary syndromes. Timely reperfusion of the infarct-related coronary artery in ST-segment-elevation myocardial infarction both with fibrinolysis or percutaneous coronary intervention minimizes myocardial damage, reduces infarct size, and decreases morbidity and mortality. Primary percutaneous coronary intervention is the preferred reperfusion method if it can be performed in a timely manner. Strategies to reduce health system-related delays in reperfusion include regionalization of ST-segment-elevation myocardial infarction care, performing prehospital ECGs, prehospital activation of the catheterization laboratory, bypassing geographically closer nonpercutaneous coronary intervention-capable hospitals, bypassing the percutaneous coronary intervention-capable hospital emergency department, and early and consistent availability of the catheterization laboratory team. With implementation of such strategies, there has been significant improvement in process measures, including door-to-balloon time. However, despite reductions in door-to-balloon times, there has been little change during the past several years in in-hospital mortality, suggesting additional factors including patient-related delays, optimization of tissue-level perfusion, and cardioprotection must be addressed to improve patient outcomes further. Early angiography followed by revascularization when appropriate also reduces rates of death, MI, and recurrent ischemia in patients with non-ST-segment-elevation acute coronary syndromes, with the greatest benefits realized in the highest risk patients. Among patients with non-ST-segment-elevation acute coronary syndromes with multivessel disease, choice of revascularization modality should be made as in stable coronary artery disease, with a goal of complete ischemic revascularization.
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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 | 0.004 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.002 |
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".