Predictors of Smoking Cessation and Relapse in Cancer Patients and Effect on Psychological Variables: an 18-Month Observational Study
Bibliographic record
Abstract
BACKGROUND: Although cancer patients are generally strongly advised to quit smoking in order to improve treatment efficacy and survival, up to 68 % of patients who were smokers at the time of cancer diagnosis continue smoking. Psychological factors such as depression and anxiety are likely to be associated with smoking behavior following a cancer diagnosis, but the empirical evidence is scarce. PURPOSE: This observational study aimed at estimating smoking cessation rates and assessing the effect of smoking cessation on psychological symptoms, as well as the predictive role of the same psychological variables on smoking cessation and smoking relapse following cancer surgery. METHODS: As part of a larger prospective, epidemiological study, smokers (n = 175) with a first diagnosis of nonmetastatic cancer completed the Hospital Anxiety and Depression Scale, the Insomnia Severity Index, and the Fear of Cancer Recurrence Inventory. Quitters (n = 55) and pair-matched nonquitters (n = 55) were compared on each symptom at pre-quitting, post-quitting, and at a 4-month follow-up. Predictors of smoking cessation and smoking relapse, including psychological variables, were also investigated. RESULTS: Fifty-five patients (31.4 %) stopped smoking at least on one occasion during the study. Of the 55 quitters, 27 (49.1 %) experienced a relapse. At pre-quitting, quitters had significantly higher levels of anxiety (p = .03) and fear of cancer recurrence (p = .01) than nonquitters, symptoms that significantly diminished at post-quitting and 4 months later in this subgroup of patients. Having breast cancer significantly predicted smoking cessation (relative risk [RR] = 3.08), while depressive symptoms were a significant predictor of smoking relapse (RR = 1.07). CONCLUSIONS: This study highlights the importance of psychological symptoms in predicting tobacco cessation and relapse among individuals with cancer. Our findings suggest that breast cancer patients are more inclined to stop smoking than patients with other cancers, but future studies should attempt to delineate the effect on smoking cessation of gender and other demographics that characterize this subgroup. This study also suggests that a particular attention should be paid to the early management of depressive symptoms in order to prevent smoking relapse.
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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 | 0.001 | 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".