Smoking status and sex as indicators of differences in 2582 obese patients presenting for weight management
Bibliographic record
Abstract
BACKGROUND: Smoking remains the most common preventable cause of death. Very little tobacco exposure can increase cardiovascular disease risk. The relationship between smoking, sex, and weight remains unclear. METHODS: Between September 1992 and June 2007, 2582 consenting patients starting the Ottawa Hospital Weight Management program were grouped by sex and smoking status. "Former smokers" (771 females, 312 males) had quit for at least 1 year. "Smokers" (135 females, 54 males) smoked > 9 cigarettes daily. There were 979 females and 331 males who never smoked. Using SAS 9.2 statistical software, the prevalence of coronary artery disease (CAD), type 2 diabetes (T2DM), major depressive disorder (MDD), and medication use among the groups was compared (Chi-square [χ(2)]). Anthropometric measurements, lipid, glucose and thyroid levels were compared using analysis of variance (ANOVA). Interactions were assessed using 2-way ANOVA analysis for continuous data, and logistic regression for discrete data. RESULTS: Smokers were more likely to have MDD (χ(2)), lower high-density lipoprotein levels and higher triglyceride levels than other groups. Former smokers had a greater prevalence of CAD, T2DM on pharmacotherapy, and impaired fasting glucose than other groups. They were also more likely to be taking lipid-lowering agents and antihypertensives (χ(2)). Never smokers had less MDD, CAD, and were less likely to be on antidepressants than the other groups. Males were more likely to have CAD and T2DM than females. Females were more likely to have MDD than males. Interactions between smoking status and sex were found for age, weight, fasting glucose and thyroid-stimulating hormone levels. CONCLUSION: Obese never smokers suffer from the fewest chronic diseases. Obese former smokers have a greater prevalence of CAD, T2DM on pharmacotherapy, and impaired fasting glucose than other groups. Thus, clinicians and researchers should avoid combining former smokers with never smokers as "nonsmokers" in research and treatment decisions. The results of this study call for a longitudinal study comparing these groups over the weight management program.
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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 | 0.000 |
| 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".