Bupropion, smoking cessation, and health-related quality of life following an acute myocardial infarction.
Bibliographic record
Abstract
BACKGROUND: The use of bupropion, a smoking cessation aid, has been associated with improved health-related quality of life (HRQOL) in the general population of smokers; but, its effect on HRQOL in post-myocardial infarction (MI) patients remains unknown. OBJECTIVES: To examine the effect of bupropion on HRQOL in post-MI patients who are attempting to quit smoking. METHODS: To accomplish this objective, we used data from a randomized, double-blind, placebo-controlled trial in 392 hospitalized post-MI patients. Treatment duration was 9 weeks, and follow-up was 12 months. HRQOL was assessed via the EuroQol-5D (EQ-5D) questionnaire, which includes 5 dimensions (mobility, self-care, daily activities, pain/discomfort, and anxiety/depression). Analyses were restricted to patients (n=225) who completed the EQ-5D at baseline, 6 months, and 12 months. RESULTS: Patients randomized to bupropion (n=109) and those randomized to placebo (n=116) experienced similar improvements in HRQOL during follow-up (difference in change in EQ-5D index from baseline to 6 months = 0.02, 95% confidence interval [CI] = -0.04, 0.08; from baseline to 12 months = 0.02, 95% CI = -0.04, 0.08). No between-group differences were observed in any of the 5 dimensions. Similar improvements in HRQOL were observed between patients who remained abstinent and those who relapsed. Lower baseline HRQOL, defined as having a HRQOL that was less than the median value, was associated with decreased smoking abstinence at 12 months follow-up (odds ratio OR =0.39, 95% CI = 0.22, 0.68). CONCLUSIONS: Bupropion does not improve HRQOL among patients attempting to quit smoking post-MI.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".