Developing a comprehensive smoking cessation program in patients with lung cancer: The role of social smoking environments.
Bibliographic record
Abstract
75 Background: Smoking during cancer treatment negatively impacts outcome, survival, and quality of life. Social smoking environments (SSEs) (i.e., smoking in household, peers, and spouse) influence cessation rates in non-cancer patients, but are understudied in cancer patients. Methods: Lung cancer patients, recruited from Princess Margaret Hospital (2006-2012) were given baseline and follow-up questionnaires (median of 2 years apart) evaluating changes in smoking habits and SSEs. Multivariate logistic regression and Cox-proportional hazard models evaluated the association of socio-demographics, clinicopathological and SSE factors with smoking cessation and time to quitting, respectively. Results: 721 patients completed both questionnaires. Of the 261 current smokers at diagnosis, 180 (69%) had quit by follow-up. Among 318 ex-smokers, 5 re-started smoking after diagnosis. All of the 140 never smokers remained non-smoking. Home smoke exposure (OR=9.4; 95% CI: 3.4-26.2; p=2.0 x 10E-5), spousal smoking (OR=4.7, 95% CI:1.7-12.6; p=3.0 x 10E-3) and peer smoking (OR=2.6; 95% CI:1.1-6.1; p=0.03) were each associated with reduced cessation, adjusted for a base multivariate model that included education and past history of depression. Individuals with no SSE factors had a much higher chance of quitting smoking when compared to patients with multiple areas of SSEs (0 vs. 3, OR=16.4; 95% CI: 4.1-66.7; p=7.3 x 10E-5). Similar results were seen when using time-to-quitting as the outcome (0 vs 3, OR=4.4, 95% CI=1.4-14.1, p=0.01). Time to quitting analysis found that 60% of patients with at least one SSE who did quit, did so within 6 months of diagnosis. Subgroup analysis revealed similar associations in early- and late-stage patient groups. Conclusions: SSE is a key factor in smoking cessation, where household smoke exposures reduces the chance of quitting up to 9-fold. SSEs should be a key consideration when developing smoking cessation programs in lung cancer patients, as part of quality improvement strategies. Approaches incorporating household members or spouses into the smoking cessation intervention, around the time of diagnosis, should be researched further. GL and WX are co-senior authors.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".