Differences in perceptions of smoking and second-hand smoke (SHS) in palliative and nonpalliative patients with cancer.
Bibliographic record
Abstract
251 Background: With improvements in cancer therapies, palliative patients with cancer now enjoy improved and sometimes prolonged survival. Continued smoking after a cancer diagnosis negatively impacts treatment response/toxicities, survival and quality of life and is influenced by SHS exposure. Little is known about the perceptions of palliative patients with cancer in comparison to patients who are considered potentially curative, on smoking after a cancer diagnosis and of SHS exposure. We assessed such potential differences in perception. Methods: Patients with cancer across all sites were surveyed with respect to their smoking habits and perceptions on how smoking and SHS influences cancer-related QofL, fatigue and overall survival (OS). Review of patient charts confirmed which patients were considered palliative versus potentially curative. Multivariable logistic regression models assessed for associations between treatment intent and patient perceptions, adjusted for significant co-variables. Results: Among 985 patients with cancer, 22% were considered palliative; 23% of surveyed patients smoked at diagnosis; 10% continued smoking at follow-up. Most patients perceived that continued smoking and SHS exposure negatively impacted QofL (continued smoking: 83%, SHS: 82%), fatigue (83%, 79%) and OS (86%, 81%). Palliative patients were more likely to believe that SHS worsened their cancer-related fatigue (adjusted odds ratio (aOR) = 1.65, 95% CI [1.05-2.56], P = 0.03) and worsened OS (unadjusted OR = 1.92, [1.12-3.33], P= 0.02; aOR = 1.56 [0.98-2.50], P = 0.06). Yet palliative/non-palliative status was not found to be associated with perceived benefits of smoking cessation on QofL, fatigue, or OS (P > 0.10, all comparisons). Conclusions: When compared with non-palliative patients, palliative patients with cancer perceived a greater negative impact of SHS on fatigue and survival, but had similar views of continued smoking after a cancer diagnosis. We are encouraged that palliative status did not lead to patients having diluted perceptions on the negative impact of smoking on cancer outcomes. Health care providers should continue to focus on the positive impacts of smoking cessation and SHS in this setting.
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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 | 0.004 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".