Effectiveness of a low-intensity smoking cessation intervention for hospitalized patients
Bibliographic record
Abstract
Debate exists about how intense smoking cessation interventions for hospitalized patients should be. In this study we assessed the effectiveness of a low-intensity smoking cessation intervention for hospitalized patients, without follow-up phone calls. We designed a cohort study with a historical control group, in the Department of Medicine of an 850-bed teaching hospital. One hundred and seventeen consecutive eligible smokers received the intervention, and 113 smokers hospitalized before the implementation of the intervention constituted the historical control group. The 30-min smoking cessation intervention was performed by a trained resident without any follow-up contact. Counseling was matched to smokers' motivation to quit, and accompanied by a self-help booklet. Nicotine replacement therapy was prescribed when indicated. All patients received a questionnaire to evaluate their smoking habits 6 months after they left hospital. We counted patients lost to follow-up as continuous smokers and smoking abstinence was validated by patients' physicians. Validated smoking cessation rates were 23.9% in the intervention group and 9.7% in the control group (odds ratio 2.9, 95% confidence interval: 1.4-6.2). After adjusting for potential confounders, intervention was still effective with an adjusted odds ratio of 2.26 (95% confidence interval: 1.04-4.95). Among those who continued to smoke 6 months after hospitalization, the likelihood of reporting any decrease of cigarette consumption was higher in the intervention cohort (70.8 vs. 42.7%, P=0.001). A low-intensity smoking cessation intervention, based on two visits without any follow-up contact, is associated with a higher quit rate at 6 months than that for historical control patients. Our findings show that a low-intensity smoking cessation intervention, based on two visits without any follow-up contact, is associated with a higher quit rate at 6 months than that for historical control patients.
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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 | 0.009 |
| 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 | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".