Elimination of second-hand smoke (SHS) exposure after a lung or head and neck (HN) cancer diagnosis and subsequent patient smoking cessation.
Bibliographic record
Abstract
183 Background: Exposure to SHS after a cancer diagnosis is associated with continued smoking in lung and HN cancer patients (PMID: 24419133, 23765604). However, smoking is a social activity. We evaluated whether elimination of SHS exposure around and after a diagnosis of lung or HN cancer is associated with smoking cessation in the cancer patient. Methods: Lung and HN cancer patients from Princess Margaret Cancer Centre (2006-12) completed questionnaires at diagnosis and follow-up (median 2 years apart) that assessed smoking history and SHS exposures (cohort study). Multivariate logistic regression analysis evaluated the association of elimination of SHS exposure after a diagnosis of cancer with subsequent smoking cessation, adjusted for significant covariates. A cross-sectional study (2014-15) of 183 lung and HN smoking patients assessed consistency in associations and interest in SHS cessation programs. Results: For the cohort study, 261/731 lung and 145/450 HN cancer patients smoked at diagnosis; subsequent quit rates were 69% and 50% respectively. 91% of lung and 94% of HN cancer patients were exposed to SHS at diagnosis while only 40% (lung) and 62% (HN) were exposed at follow-up. Elimination of SHS exposure was associated with smoking cessation in lung (aOR = 4.76, 95% CI [2.56-9.09], P< 0.001), HN (aOR = 5.00 [1.61-14.29], P< 0.001), and combined cancers (aOR = 5.00 [3.03-8.33], P< 0.001). The cross-sectional study has similar cessation and SHS exposure rates and a similar association for elimination of SHS with smoking cessation (aOR = 3.42 [1.16-10.10], P= 0.03). However when asked directly, only 26% of patients quit smoking with another individual and 13% of patients exposed to SHS had at least 1 interested party in joining a SHS cessation program. Conclusions: Elimination of SHS exposure around patients is significantly associated with smoking cessation in lung and HN cancer patients, but few patients quit smoking together with others around them, despite the ‘teachable moment’ with a cancer diagnosis. Clinicians should encourage patients and their household/friends to quit smoking together to improve cessation rates in cancer patients and those around them.
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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 | 0.000 | 0.001 |
| 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".