The role of second‐hand smoke exposure on smoking cessation in non–tobacco‐related cancers
Bibliographic record
Abstract
BACKGROUND: Second-hand smoke (SHS) is a significant barrier to smoking cessation after a diagnosis of cancer in patients with lung as well as head and neck cancers. In the current study, the authors evaluated the effect of SHS on smoking cessation among patients with those cancers not traditionally perceived to be strongly associated with smoking. METHODS: Patients recruited from a single tertiary care center completed a self-administered questionnaire. Multivariate logistic regression and Cox proportional hazards models evaluated the association of sociodemographics, clinicopathological variables, and exposure to SHS with either smoking cessation or time to quitting. RESULTS: In all, 926 patients with diverse cancer subtypes completed the questionnaire. Of the 161 who were current smokers at the time of their cancer diagnosis, 48% quit after diagnosis. Lack of exposure to SHS at home was found to be associated with smoking cessation at any time after diagnosis (adjusted odd ratio, 4.28; 95% confidence interval, 1.56-11.78 [P =.005]), with similar trends noted 1 year after diagnosis (adjusted odds ratio, 2.56; 95% confidence interval, 0.91-7.22 [P =.08]). There was a significant inverse dose-response relationship between hours of SHS exposure at home and smoking cessation. Spousal and peer smoking were not found to be significantly associated with smoking cessation on multivariate analysis (P>.05). Kaplan-Meier analysis found that of patients who did quit smoking, 61% quit within 6 months of their cancer diagnosis. CONCLUSIONS: Exposure to SHS at home is a significant barrier to smoking cessation in patients whose cancers are not traditionally perceived as being related to tobacco. SHS should be a key consideration in the development of survivorship programs geared toward smoking cessation for all patients with cancer.
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".