Systematic approaches to smoking cessation in the cardiac setting
Bibliographic record
Abstract
PURPOSE OF REVIEW: Constituents of tobacco smoke are prothrombotic and atherogenic and causative factors in the development of coronary heart disease (CHD). Smoking cessation is the single most important intervention to reduce morbidity and mortality in smokers with CHD. This review presents contemporary information regarding treatments for smoking cessation in the setting of CHD. RECENT FINDINGS: The beneficial effects of smoking cessation may be mediated by improvements in endothelial function. Failure to quit smoking in those with CHD is a typical consequence of nicotine addiction. Practical counseling and pharmacotherapy [nicotine replacement therapy (NRT), bupropion, and varenicline] are well tolerated and effective treatments for CHD patients attempting to quit smoking. Treatments initiated in hospital following a CHD-related event or procedure are more effective than those initiated outside the hospital setting. Extending medication use beyond the initial treatment phase is the most promising means of preventing relapse. Financial coverage for smoking cessation pharmacotherapy improves quit rates. The routine provision of pharmacotherapy and practical counseling in the CHD setting can be assured by implementing proven, systematic approaches to smoking cessation treatment. SUMMARY: Smoking cessation is a fundamental priority in smokers with CHD. Systematic approaches to ensure that cessation assistance is provided by clinicians and to improve cessation outcomes for smokers are effective and available.
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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 | 0.007 | 0.030 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.003 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".