Contemporary pharmacological reperfusion in ST elevation myocardial infarction
Bibliographic record
Abstract
PURPOSE OF REVIEW: Fibrinolysis remains a key therapeutic alternative mode of reperfusion in patients with ST segment elevation myocardial infarction (STEMI). Its venerability relates to the wealth of clinical efficacy evidence, ease of administration, and broad applicability to the large number of patients who cannot receive mechanical reperfusion within a reasonable period of time. This review focuses on recent data that will further enhance the clinician's ability to deliver a pharmacological reperfusion strategy to this patient population. RECENT FINDINGS: Combined data from clinical trials as well as registry data support implementation of the guideline endorsed pharmacoinvasive strategy for patients unable to achieve rapid primary percutaneous coronary intervention. The most appropriate mode of reperfusion remains dependent upon the time from symptom onset to presentation as well as perceived delay to initiation of mechanical reperfusion therapy, and one strategy does not fit all patients at all times. Additional information is required in the growing population of elderly patients with STEMI to identify the most appropriate approach to reperfusion in this high-risk population. SUMMARY: Despite extensive investigation concerning the optimal management of STEMI over the last three decades, significant knowledge gaps exist and the efficient application of current evidence to clinical practice remains elusive.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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 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".