Second-Hand Smoke As a Predictor of Smoking Cessation Among Lung Cancer Survivors
Bibliographic record
Abstract
PURPOSE: Second-hand smoke (SHS; ie, exposure to smoking of friends and spouses in the household) reduces the likelihood of smoking cessation in noncancer populations. We assessed whether SHS is associated with cessation rates in lung cancer survivors. PATIENTS AND METHODS: Patients with lung cancer were recruited from Princess Margaret Cancer Centre, Toronto, ON, Canada. Multivariable logistic regression and Cox proportional hazard models evaluated the association of sociodemographics, clinicopathologic variables, and SHS with either smoking cessation or time to quitting. RESULTS: In all, 721 patients completed baseline and follow-up questionnaires with a mean follow-up time of 54 months. Of the 242 current smokers at diagnosis, 136 (56%) had quit 1 year after diagnosis. Exposure to smoking at home (adjusted odds ratio [aOR], 6.18; 95% CI, 2.83 to 13.5; P < .001), spousal smoking (aOR, 6.01; 95% CI, 2.63 to 13.8; P < .001), and peer smoking (aOR, 2.49; 95% CI, 1.33 to 4.66; P = .0043) were each associated with decreased rates of cessation. Individuals exposed to smoking in all three settings had the lowest chances of quitting (aOR, 9.57; 95% CI, 2.50 to 36.64; P < .001). Results were similar in time-to-quitting analysis, in which 68% of patients who eventually quit did so within 6 months after cancer diagnosis. Subgroup analysis revealed similar associations across early- and late-stage patients and between sexes. CONCLUSION: SHS is an important factor associated with smoking cessation in lung cancer survivors of all stages and should be a key consideration when developing smoking cessation programs for patients with lung cancer.
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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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
| 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".