Gender Difference and Characteristics Attributed to Coronary Artery Disease in Gaza-Palestine
Bibliographic record
Abstract
UNLABELLED: Traditionally coronary artery disease (CAD) has been considered as disease affecting men, and for long time women were not included in researches programme. In both sexes, coronary heart disease risk increases with age. Extensive clinical and statistical studies have identified serial factors that increase the risk of coronary heart disease, some of them can be modified, and some cannot. This study was performed to analyze the extent to which cardiovascular risk factors can explain the gender difference in coronary heart disease. METHODS: The study design is a cross sectional study based on 155 cardiac patients admitted to cardiology department in Al-shifa hospital Gaza. The following cardiac risk factors were determined from the patient's records, smoking, diabetes, high blood pressure, Dyslipedemia and presence of family history of coronary artery disease. Catheterization results review were done. Statistical Package for Social Science version 17 was used for data entry and analysis. Frequency and cross tabulation were done to explore the relationship between the study variables. Chi-square test was used for testing statistical and P-value less than 0.05 were considered as significant. RESULTS: Most of risk factors were more favorable in females and increase with age. Myocardial infarction in male compared with female was 2 times higher, and chronic angina pain is common in female than male respectively 71.4% and 46.7%. Around 77% of female have two vessels disease and more. No great differences in number of diseased vessels among patients with myocardial infarction or chronic stable angina. Patients with low EF <50% have higher chance of affected vessels (82.9%). CONCLUSION: CAD stay the major problem in male and female, certain patient's characteristics and clinical conditions may place female at higher risk of coronary artery disease development or progression. This article addresses emerging knowledge regarding gender differences in CAD risk factors and responsiveness to risk reduction interventions.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 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) | 0.000 | 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".