Metabolic syndrome in Iran
Bibliographic record
Abstract
Metabolic Syndrome (MS) also known as syndrome X, the Dysmetabolic Syndrome and Insulin resistance syndrome, refers to a cluster of cardiovascular risk factors including hypertension, glucose intolerance, triglyceridemia and low HDL cholesterol concentrations in blood. This syndrome consists of multiple metabolic risk factors. The significance of MS is that MS and its components seem to be underlying factors for the development of atherosclerotic cardiovascular disease and diabetes type 2. The current article reviews the literature on the prevalence of MS in Iran. According to global statistics, a quarter of the adult population suffers from metabolic syndrome. The prevalence of MS in United States is 24% and 44% of adults over 50 years old suffer from MS. In contrast to this high prevalence of MS in United States of America, its prevalence in some countries such as South Korea is less than 14.2% in men. However, its prevalence in two neighbor country of Iran including Saudi (39.3%) and Turkish (33%) populations is relatively high compared to other countries. We know that 30% of adults in Tehran (Capital city of Iran) suffer from MS and more than 45% of adults older than 20 years old in the Khorasan province (in north east Iran) have MS. This statistics reveal that the prevalence of MS in Iran is even higher than the developed countries and the relation between MS and Coronary Artery disease suggests that we need to continue research on MS, its components, and the association between MS and Coronary Artery Disease.Key words: Metabolic syndrome, Coronary artery disease, Risk factor
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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 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".