Randomized trial of a smoking cessation intervention in hospitalized patients
Bibliographic record
Abstract
A hospitalization is a time when perceived vulnerability to dangers from smoking and quitting motivation may be at their peak. Aim was to determine whether a smoking cessation intervention of moderate intensity would increase the smoking cessation rate in hospitalized smokers. Design was randomized trial, conducted in a university-affiliated cardio-pulmonary tertiary care center. Participants were hospitalized smokers aged < or =70 years. Intervention was a smoking cessation intervention consisting of education and psychological support, with or without pharmacological therapy, associated with follow-up phone calls. Patients assigned to the control group received usual care. Measurement was point prevalence cessation rate at 1-year follow-up. A total of 468 patients were screened; 196 were randomized. Although the smoking cessation rates at 12-month follow-up were higher than expected, we found no significant difference between the study groups (intervention: 30.3%; control: 27.8%). Similar results were obtained in patients whose smoking status was validated by urinary cotinine assay. Length of stay and dependence to nicotine were the only significant predictors of smoking cessation. A smoking cessation intervention of moderate intensity delivered in a tertiary cardio-pulmonary center did not increase the smoking cessation rate at 1-year follow-up. The results of this trial should not divert those who deliver care to inpatients from delivering a brief smoking cessation intervention.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.004 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 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".