Survey of the Effect of Streptokinase on Ventricular Repolarization by Examining the QT Dispersion in Patients With Acute Myocardial Infraction in Seyed-Al-Shohada Hospital, Urmia
Bibliographic record
Abstract
Cardiovascular events are the most common cause of morbidity and mortality throughout the world and myocardial infarction is the most common cause of these accidents. Myocardial infarction impairs the mechanical and electrical activity of the heart that these disorders predispose the patient to cardiac arrhythmias including ventricular tachycardia. QT dispersion is an important parameter to evaluate the heterogeneity of ventricular repolarization that minimal and the maximum interval is QTc in 12-lead EKG. In this study, 200 patients with the diagnosis of acute myocardial infraction with ST-segment elevation were hospitalized and treated with streptokinase. Patient records were extracted from the medical records department. EKG was studied before receiving streptokinase, an hour after receiving streptokinase and 4 days later for calculating and comparing QTd. It was concluded that QTd mean in EKG one hour after receiving streptokinase is decreased compared to pre-operation but this decline is not statistically significant. QTd mean in EKG day 4 after MI is slightly increased compared to the baseline, which is not statistically significant.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".