Association of Smoking with General and Abdominal Obesity: Evidence from a Cohort Study in West of Iran.
Bibliographic record
Abstract
BACKGROUND: This study aimed to examine the association between smoking and obesity among adults in Kermanshah Province, west of Iran. STUDY DESIGN: A cross-sectional study. METHODS: A total of 8822 participants, aged 35-65 yr, form Ravansar Non-communicable Disease (RaNCD) cohort study (2014-2016) were enrolled. Smoking habits were categorized in terms of smoking status (current, former and never smokers) and smoking intensity (light, moderate and heavy). General obesity was defined as body mass index (BMI) ≥30 kg/m2 and abdominal obesity were defined as a waist to hip ratio (WHR) ≥90 for men and ≥85 for women. Multiple logistic regressions were used to examine the association between general and abdominal obesity with smoking status and smoking intensity while controlling for age, sex, years of education and wealth index. RESULTS: Overall, 12% were current smokers, 8.4% former smokers and 79.6% never smokers. The prevalence of light, moderate and heavy smokers among current smokers was 30.8%, 18%, and 51.2%, respectively. The prevalence of general obesity was 27.6%, while the prevalence of abdominal obesity was 82.3%. The probabilities of general and abdominal obesity for current smokers were lower than never smokers by 34% and 36%, respectively. The probability of abdominal obesity for heavy smokers was 31% lower than light smokers. We did not observe significant associations between smoking intensity and general obesity. CONCLUSIONS: Current smokers compared to never smokers were less likely to be obese. The reverse association between smoking and obesity; however, should not be interpreted as a causal relationship.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.001 | 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".