Social environment, secondary smoking exposure, and smoking cessation among head and neck cancer patients
Bibliographic record
Abstract
BACKGROUND: Smoking during treatment of squamous cell head and neck cancer (HNC) has adverse affects on toxicity, treatment, and survival. The purpose of this report was to evaluate sociodemographic predictors of smoking cessation in HNC patients to support the development of a smoking cessation program. METHODS: Newly diagnosed HNC patients (2007-2010) at Princess Margaret Cancer Centre treated with curative intent were prospectively recruited. Patients completed self-reported baseline and follow-up questionnaires, assessing changes in social habits. Predictors of smoking cessation and time to quitting were evaluated using logistic regression and Cox proportional hazard models, respectively. RESULTS: Of 295 HNC patients, 49% were current smokers at diagnosis, and 50% quit after diagnosis. These individuals were more likely to have smoked for fewer years (P = .0003), never used other forms of tobacco (P = .0003), and consumed less alcohol (P = .002). No cigarette exposure at home (OR, 7.44 [3.04-18.2]), no spousal smoking (OR, 4.25 [1.70-10.6]), and having fewer friends who smoke (OR, 2.32 [1.00-5.37]) were consistent predictors of smoking cessation after diagnosis. Having none of these exposures (OR, 13.8 [4.13-46.0]) and seeing a family physician (OR, 3.92 [1.38-11.2]) were independently associated with smoking cessation and time-to-quitting analyses. Most HNC patients (68%) quit within 6 months of diagnosis. Patients who were ex-smokers at diagnosis were older (P < .0001), more likely to be female (P = .0002), more likely to be married (P = .0004), more educated (P = .01), and had fewer pack-years of smoking (P < .0001). CONCLUSIONS: Spousal smoking, peer smoking, smoke exposure at home, and seeing a family physician were strongly and consistently associated with smoking cessation and time to quitting after a HNC diagnosis.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| 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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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 teacher head, 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".