Effect of fibrinolytic therapy on ST-elevation myocardial infarction clinical outcomes during the COVID-19 pandemic: A systematic review and meta-analysis
Bibliographic record
Abstract
BACKGROUND: ST-elevation myocardial infarction (STEMI) is the result of transmural ischemia of the myocardium and is associated with a high mortality rate. Primary percutaneous coronary intervention (PPCI) is the recommended first-line treatment strategy for patients with STEMI. The timely delivery of PPCI became extremely challenging for STEMI patients during the coronavirus disease 2019 (COVID-19) pandemic, leading to a projected steep rise in mortality. These delays were overcome by the shift from first-line therapy and the development of modern fibrinolytic-based reperfusion. It is unclear whether fibrinolytic-based reperfusion therapy is effective in improving STEMI endpoints. AIM: To determine the incidence of fibrinolytic therapy during the COVID-19 pandemic and its effects on STEMI clinical outcomes. METHODS: PubMed, Google Scholar, Scopus, Web of Science, and Cochrane Central Register of Controlled Trials were queried from January 2020 up to February 2022 to identify studies investigating the effect of fibrinolytic therapy on the prognostic outcome of STEMI patients during the pandemic. Primary outcomes were the incidence of fibrinolysis and the risk of all-cause mortality. Data were meta-analyzed using the random effects model to derive odds ratios (OR) and 95% confidence intervals. Quality assessment was carried out using the Newcastle-Ottawa scale. RESULTS: < 0.001) with all-cause mortality. CONCLUSION: There is an increased incidence of fibrinolysis during the pandemic period, but it has no effect on the risk of all-cause mortality. The low- and middle-income status has a significant impact on the all-cause mortality rate and the incidence of fibrinolysis.
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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 | 0.013 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.019 | 0.009 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".