Effect of Pre-hospital Thrombolysis on Mortality in Patients with Myocardial Infarction: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
Background and purpose: Early treatment of thrombolysis can reduce mortality in patients with myocardial infarction, so, this systematic review and meta-analysis aimed to compare the effect of pre-hospital thrombolysis and in-hospital thrombolysis on short-term mortality in patients with myocardial infarction. Materials and methods: Systematic search was conducted in electronic databases including Pubmed, Web of Science, Cochrane, and Embase without time and language constraints using related keywords. All articles were exported to EndNote. In initial search, 223 articles were found but finally 10 articles were selected for quality assessment, which was performed using the JADAD standard checklist for interventional studies and the Newcastle Ottawa Scale checklist for cohort studies. Comprehensive Meta-Analysis software was used for data analysis. Results: The total samples size was 4291 in the pre-hospital group and 4,730 in the in-hospital group. Three clinical trials and all three cohort studies were found to have a good quality. Meta-analysis showed that thrombolytic therapy at the onset of symptoms and prior to patient's transfer to hospital could reduce mortality by 36% (OR= 0.64 CI 95%: 0.45-0.91). Conclusion: Pre-hospital thrombolysis by emergency technicians can reduce mortality in patients with myocardial infarction.
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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 | 0.017 | 0.043 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.019 | 0.048 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".