Social environment as a predictor of smoking cessation and recidivism in lung cancer survivors.
Bibliographic record
Abstract
9032 Background: Smoking during cancer treatment negatively impacts treatment, survival and quality of life. Lung cancer patients with a smoking history often continue to smoke; some ex-smokers re-start after diagnosis. Social environment impacts cessation and recidivism rates in non-cancer patients. We assessed whether the same influences occur among lung cancer patients. Methods: Lung cancer patients, recruited from Princess Margaret Hospital, completed a baseline questionnaire about their demographics and smoking history (at diagnosis). A follow-up questionnaire was administered at a median of two years, assessing changes in smoking habits, exposure at home/work/among friends, healthcare use, social support and alcohol use since diagnosis. The relationship between each variable with cessation/recidivism was analyzed. Odds ratios (OR) and 95% confidence intervals (95% CI) were calculated. Results: 478 patients completed both questionnaires. Of the 100 current smokers at diagnosis; 52 quit by the time of the follow-up questionnaire. Among 294 ex-smokers, 15 started to smoke after diagnosis. None of the 84 never smokers at baseline started to smoke after diagnosis. Exposure to smoking at home was associated with continued smoking and relapse (OR=5.1, 95% CI: 1.8–14.3, p=0.001; and OR=3.9, 95% CI: 0.8–14.4, p=0.04, respectively). Specifically, spousal smoking was associated with both continued smoking (OR=7.3, 95% CI: 2.4–21.7, p=2.0E-04) and recidivism (OR=3.7, 95% CI: 0.6–16.6, p=0.08). Having more than a few friends who smoke is associated with continued smoking (OR=3.5, 95% CI: 1.4–8.7, p=0.005) and relapse (OR=4.8, 95% CI: 1.5–15.0, p=0.004). Not completing high school was also associated with continued smoking (OR=3.0, 95% CI: 1.2–7.6, p=0.02). Multivariate analysis identified spousal smoking as the major single predictor of continued smoking (OR=8.8, 95% CI: 2.2–34.8, p=0.002). Conclusions: Smoking cessation programs for lung cancer patients should not only target the patient but also include the immediate family, consider a patient’s peers and be tailored to the patient’s education level. Involvement of the immediate family and consideration of peers may help prevent smoking relapse.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".