ATHEROSCLEROSIS RISK FACTORS FREQUENCY AMONG NORTH AMERICANs AND IRANIANs STROKE CASES
Bibliographic record
Abstract
Abstract INTRODUCTION: Atherosclerosis of cerebral arteries is the most common etiology of ischemic stroke around the world. This pilot double-center study evaluated risk factors of atherosclerosis in stroke patients in two different racial subtypes. METHOD AND MATERIALS: This prospective clinical study was conducted on 100 consecutive stroke patients in Mackenzie hospital, Canada and 100 consecutive stroke patients in Ghaem hospital, Iran in 2007. The patients were age and sex matched. All of the Canadian patients were from white North American race and all of the Iranian patients were white Persian race. Diagnosis of ischemic stroke was made by stroke neurologists. The frequency rates of hypertension, diabetes, hypercholestrolemia and smoking were detected in the two studied groups. Chi-Square and Fisher tests served for statistical analysis and P < 0.05 was declared as significant. RESULTS: 92 males and 108 females with ischemic stroke were investigated. Hypertension was the most common risk factor of atherosclerosis in all patients followed by hypercholestrolemia, diabetes and smoking. The influence of race on the frequency rate of atherosclerosis risk factors was not significant, P > 0.05. Hypertension was significantly more frequent in Iranian than Canadian males: df = 1, P = 0.023. However, racial difference in the frequency rate of the hypertension was insignificant in the females: df = 1, P = 0.841. The effects of race on frequency rate of other atherosclerosis risk factors was insignificant in each gender separately, P > 0.05. CONCLUSION: There is no significant difference in frequency rate of atherosclerosis risk factors between North American and Persian stroke patients. Keywords: Atherosclerosis, Risk Factors, Stroke, Race.
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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 | 0.001 | 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 | 0.000 |
| 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".