Socioeconomic determinants and metabolic syndrome: Results from the Isfahan Healthy Heart Program.
Bibliographic record
Abstract
Introduction: The prevalence of metabolic syndrome (MetS) is increasing in Iran. We assessed the relationship between socioeconomic status (SES) and Mets components in the Iranian population. MATERIALS AND METHODS: The sample for this study comprised a random cross-section of men and women from two province districts who participated in the Isfahan Healthy Heart Program (IHHP) in 2007. Each participant completed a questionnaire, underwent anthropometric testing and blood pressure measurements, and provided a blood sample. Mets was defined based on ATPIII criteria. Several SES dimensions, such as education, occupation, and number of children, as well as home, car, and personal computer ownership, were assessed to determine the participant's SES. RESULTS: A higher-than-average income, car ownership, owning or renting a private home, and having a computer are increasing towards increment in SES. All MetS components were more prevalent in participants defined as having a lower SES, while low HDL levels were more common in participants having an SES II (P>0.001). A multivariate analysis showed that having the lowest SES (I) increased the risk of MetS by 1.72 [1.44-2.07], whereas subjects having an SES III had a 1.23 [1.04-1.47] lower risk for MetS. CONCLUSIONS: The relationship between SES and Mets is due largely to behavioural factors, such as practicing unhealthy eating habits. Given the high prevalence of Mets in Iran, we propose that regular health check-ups may be useful in the early detection of the syndrome and, consequently, in the prevention of its effects. In addition, the early detection of MetS may result in the early diagnosis and prevention of cardiovascular diseases.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".