Abstract P323: The Effect of Smoking Cessation on Weight at 12 Months in Patients Post-Myocardial Infarction
Bibliographic record
Abstract
Background: Current guidelines recommend smoking cessation and weight management for secondary prevention in post-myocardial infarction (MI) patients. However, little is known about the effects of smoking cessation on weight change post-MI. Methods: We examined this question using data from a randomized, double-blind, placebo-controlled trial investigating the effect of bupropion on smoking cessation in patients immediately following a MI. Weight change was compared between 3 groups: patients who reported complete abstinence, those who reported intermittent smoking, and those who reported persistent smoking during the 12-month follow-up. Analyses were restricted to patients who attended all follow-up visits (N=179). Weight was collected by research nurses at follow-up. Abstinence was defined by self-report in the previous 7 days and a carbon monoxide level ≤10 ppm. Results: During follow-up, 92 patients were abstinent, 49 were intermittently smoking, and 38 were consistently smoking. At baseline, 68.7% of patients were male, and the mean age was 53.9 years (SD 10.0). The mean weight and BMI at baseline were 78.4 kg (SD 17.7) and 27.3 kg/m 2 (SD 5.0), respectively. Mean body weight increased in all 3 groups during follow-up ( Figure ). However, patients who remained abstinent were more likely to gain weight than those who smoked persistently (difference 3.3 kg, 95% CI 0.9, 5.6). No difference in weight change was present between persistent and intermittent smokers. Both intermittent and persistent smokers reduced their daily cigarette consumption between baseline and 12-month follow-up (mean difference −15.5, 95% CI −19.1, −11.9 and −15.8, 95% CI, −19.9, −11.7, respectively). Conclusions: Patients who remain abstinent are more likely to gain weight 12 months post-MI. Given the importance of weight management in this population, strategies to ensure long-term weight control among patients who quit smoking are needed.
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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".