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 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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| 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 | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".