Smoking Cessation in Patients Diagnosed with Head and Neck Cancer
Bibliographic record
Abstract
OBJECTIVE: To determine patients' smoking status after the diagnosis and treatment of squamous cell carcinoma of the head and neck (SCCHN) and to identify factors associated with smoking cessation. DESIGN: Cross-sectional survey study conducted over a 2-year period. SETTING: Head and neck surgery clinic of an academic tertiary care hospital. METHODS: Two hundred thirteen consecutive patients diagnosed with SCCHN were interviewed to ascertain patients' smoking status and the incidence of smoking cessation. Information on demographics, tobacco and alcohol history, disease characteristics, and treatment modality was also collected. MAIN OUTCOME MEASURES: The rate of smoking cessation was evaluated, in which smoking cessation is defined as the use of no cigarettes at least 1 month prior to the interview. Possible predictors of smoking cessation were evaluated. RESULTS: One hundred twenty-five patients were found to be smoking at the time of diagnosis. Among these patients, 53.6% stopped smoking after diagnosis or during treatment. In the univariate analyses, tumour site (p = .01), concurrent alcohol use (p = .03), and number of attempts to quit pre- (p = .03) and postdiagnosis (p = .001) were found to be highly predictive of patient smoking cessation. Multivariable modelling showed that gender, tumour site, and number of attempts to quit smoking were significantly and independently related to smoking cessation. CONCLUSIONS: Although smoking cessation would be presumed to be high after cancer diagnosis, this study has identified patient subgroups in which postdiagnosis smoking cessation intervention programs need to be made more effective.
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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) | 0.000 | 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".