Prediction models of smoking cessation in lung and head and neck cancer patients: Role of second-hand smoke (SHS) exposure.
Bibliographic record
Abstract
9591 Background: Some cancer survivorship programs incorporate components of healthy lifestyle behavior modification. We evaluated the role that various clinical variables and smoking habits play in predicting which cancer survivors are more likely to quit smoking. Such knowledge may help with resource allocation within these programs. Methods: We focused on lung cancer (LC) and head and neck cancer (HNC) patients as these have the highest active smoking rates among all cancer sites at Princess Margaret Cancer Centre. Patients from 2006-12 completed questionnaires at diagnosis (baseline) and follow-up (median 2 years apart) that assessed smoking status. Baseline clinical and demographic information was obtained. Multivariate logistic regression analysis evaluated the association of smoking and clinical variables at diagnosis to subsequent smoking cessation. Predictive models were assessed for their discriminatory capabilities (concordance-index or area under the curve, AUC). Results: In this cohort, 261/731 LC and 145/450 HNC patients smoked at diagnosis; subsequent overall quit rates were 69% and 50% respectively. Univariate factors associated with smoking cessation included having LC (p = 0.001), being married (p = 0.02), having at least completed secondary school (p = 0.049), having less cumulative smoking (pack-years; p = 0.004), and having adequate social support (p = 0.009). In multivariate modeling, fewer pack-years, having LC and being married remained significant and this predictive model was associated with moderate predictive ability (AUC 0.68 [95% CI: 0.62-0.73]. However, the addition of either SHS household exposure (AUC 0.76 [0.71-0.81]) or spousal smoking (AUC 0.77 [0.71-0.82]) further improved the predictive model. The addition of SHS variables in other exploratory predictive models of smoking cessation improved those models in a similar manner. Similar improvements in prediction were seen in subgroup analysis of LC and HNC. Conclusions: SHS exposure significantly improves the predictive abilities to determine which patients who smoked at their cancer diagnosis would subsequently quit. Cessation programs may benefit from allocating resources accordingly.
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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 | 0.008 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".