Smoking cessation: lessons learned from clinical trial evidence
Bibliographic record
Abstract
PURPOSE OF REVIEW: Cigarette smoking and exposure to secondhand smoke cause coronary heart disease. Cessation dramatically reduces the incidence of primary and secondary cardiac events. The review presents up-to-date information regarding nicotine dependence, recent findings related to its treatment, and recommendations for addressing smoking cessation for the primary and secondary prevention of coronary heart disease. RECENT FINDINGS: Bans on smoking in public places are associated with significant reductions in the incidence of acute myocardial infarction. Counseling and pharmacotherapy (nicotine replacement therapy, bupropion) are proven, effective treatments for nicotine dependence. Clinical trials of two new pharmacotherapies, varenicline and rimonabant, have recently been reported. Varenicline is a safe and efficacious medication for smoking cessation, and has been approved in the US, Canada and Europe. Rimonabant has shown mixed results for smoking cessation and is undergoing further evaluation. SUMMARY: All patients should be screened for tobacco use. Clinicians can effectively treat nicotine dependence in the general population using counseling and first-line pharmacotherapies (nicotine replacement therapy, bupropion, varenicline). These same treatments, with some modification, are appropriate for smokers with coronary heart disease; however, brief interventions without follow-up are not effective in this population. For smokers with coronary heart disease, the best time to intervene may be during hospitalization.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.027 | 0.110 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.006 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.005 |
| Open science | 0.004 | 0.001 |
| Research integrity | 0.005 | 0.004 |
| Insufficient payload (model declined to judge) | 0.012 | 0.002 |
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".