Smokers and Postcessation Weight Gain After Acute Coronary Syndrome
Bibliographic record
Abstract
BACKGROUND: Smoking cessation and weight management are recommended after acute coronary syndrome (ACS); however, little is known about the effects of smoking cessation on weight change after ACS. We aimed to assess the effect of smoking cessation after ACS on weight over a 12-month follow-up period. METHODS AND RESULTS: Data were prospectively collected from the EVITA (Evaluation of Varenicline in Smoking Cessation for Patients Post-Acute Coronary Syndrome) trial. Weight change was compared among 3 groups of patients: those who were completely abstinent (n=70), those who smoked intermittently (n=68), and those who smoked persistently (n=34). Patients' mean baseline weight was 83.9 kg (SD 17.7) with a mean body mass index of 28.5 (SD 5.4). Patients smoked a mean of 37.7 years (SD 17.7) and a mean of 21.0 cigarettes (SD 9.0) per day prior to their ACS. Weight change varied across groups, with abstainers gaining a mean of 4.8 kg (SD 8.6), intermittent smokers gaining a mean of 2.0 kg (SD 8.9) and persistent smokers losing a mean of 0.7 kg (SD 7.4). At 52 weeks, abstainers were more likely to gain weight than persistent smokers (difference in means 5.5 kg; 95% CI 2.3-8.8). This weight gain was not associated with an increase in the use of antihypertensive or antidiabetic medications. CONCLUSIONS: Following an ACS, significant weight is gained by patients who quit smoking. Weight-management interventions among smokers who quit after ACS should be a focus of investigation in future research so that the cardiovascular benefits achieved by smoking cessation are not offset by weight gain in this high-risk population. CLINICAL TRIAL REGISTRATION: URL: http://www.clinicaltrials.gov. Unique identifier: NCT00794573.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 | 0.001 | 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".