Ten-year Atherosclerosis Cardiovascular Disease (ASCVD) risk score and its components among nomadic population in southern Iran: A population-based study
Bibliographic record
Abstract
Introduction Prevalence of atherosclerotic cardiovascular disease (ASCVD) has increased in developing countries, such as Iran. For reducing ASCVD, epidemiological information, especially in nomadic populations, are needed. This study aimed to estimate the prevalence of ASCVD among the nomadic population. Method This cross-sectional study was conducted on 784 nomadic people aged 40–70 years in Fars province. The 10-year ASCVD risk was calculated with an estimator developed by the American College of Cardiology/American Heart Association (ACC/AHA). To determine the factors of ASCVD risk score, ordinal logistic regression was used. Statistical analyses were performed using SPSS version 16 software. Results The mean age of the subjects under study was 53.32 ± 8.94 years, of whom 432 (55.1 %) were female. Based on ordinal regression, access to the health centers (p = 0.002), diabetes (p < 0.001), MI (p = 0.023), not using Aspirin (p = 0.001), housekeeper job (p < 0.001), high LDL (p = 0.002), and physical activity (p = 0.04) were associated with a higher category of ASCVD risk score. Additionally, people in the age group of 60–69 years had the highest percentage in all ASCVD score classes except the low-risk group. Conclusion The results indicated that having modifiable risk factors increases the chance of CVD in the nomadic population, and older people are more at risk. Since this population has received less attention and plays a main role in providing food products for Iranian people, it is suggested that more attention should be paid to the health of this special group through prevention and control programs.
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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 | 0.005 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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) | 0.000 | 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".