Stepped Care Approach to Smoking Cessation in Patients Hospitalized for Coronary Artery Disease
Bibliographic record
Abstract
PURPOSE: Smoking cessation is an important goal for smokers with coronary artery disease (CAD) because it reduces cardiac morbidity and mortality. Effective interventions for cigarette smokers with CAD exist, but they often are considered to be intensive and expensive. Stepped-care interventions have been proposed as a promising way to allocate smoking cessation treatments in a cost-effective manner. Stepped care refers to the practice of initiating treatment with low-intensity intervention and then exposing treatment failures to successively more intense interventions. METHODS: To address the efficacy of this approach, 254 cigarette smokers hospitalized with CAD were provided a brief cessation intervention. The participants then were assigned randomly to either a more intensive stepped-care treatment (counseling and nicotine patch therapy) or no additional treatment. Outcomes were point-prevalent abstinence measured 3 months and 1 year after hospital discharge. RESULTS: Stepped-care treatment increased smoking cessation rates from 42% to 53% during a 3-month follow-up period (P =.05), but showed little effect at the 1-year follow-up assessment, as evidenced by a cessation rate for the minimal intervention group of 36% versus 39% for the stepped-care group (P =.36). CONCLUSIONS: A stepped-care approach to smoking cessation increased short-but not long-term point-prevalent abstinence in patients with CAD. For improvement of long-term effectiveness, refinement of the timing and content of stepped-care interventions needs to occur.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".