Effect of Streptokinase on Reperfusion After Acute Myocardial Infarction and Its Complications: An Ex-Post Facto Study
Bibliographic record
Abstract
INTRODUCTION: Emergency treatment of patients with acute myocardial infarction is very important. Streptokinase in Iran is often as the only clot-busting medication is used. The purpose of using streptokinase medication is to revive the ischemic heart tissue, although has dangerous complications too. Therefore, the present study aimed to determine the effect of streptokinase on reperfusion after acute myocardial infarction and its complications, has been designed and conducted. MATERIALS & METHODS: This is an Ex-post facto study. The study population included patients who suffer from acute myocardial infarction. The sample size was 300 patients, and 2 groups were matched, in variables of age, sex, underlying disease, frequencies and area of MI. Data collection did by researcher making questionnaire, that accept face and content validity by 10 expert researcher, the reliability was conducted with Spearman's test (r=0.85) by Test-retest method. Data analysis did by SPSS software: V 12. FINDINGS: Mean of EF in SK group was (46.15±8.11) and in control group was (43.11±12.57). Significant relationship was seen between SK, arrhythmia occurring and improve EF reperfusion by chi-square test (p=0.028), (p=0.020).The most arrhythmia in SK group was Ventricular Tachycardia (20.7%). Significant statistical relation between SK and mortality were found by Chi-square test (p=0.001). But a meaningful statistical relation was not found between SK and pulmonary edema incidence (p=0.071). CONCLUSIONS: Nurses of CCU should be aware about SK complications such as hypotension, bleeding and arrhythmias. Proposed compare SK and tissue plasminogen drug in reperfusion and complications effect.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".