Cancer patient attitudes and preferences towards smoking status assessment.
Bibliographic record
Abstract
110 Background: Continued smoking after a cancer diagnosis is associated with poorer outcomes. As smoking cessation is an important part of cancer care, understanding pt attitudes towards smoking status assessment will help with integrating smoking cessation programs into survivorship care. Methods: Cancer pts were surveyed on their smoking history, assessment rates and attitudes/preferences towards smoking status assessment. Multivariate logistic regression models assessed for factors associated with screening preferences. Results: Among 501 pts, 115 smoked at diagnosis, 60% quit after; 53% had a tobacco related (lung/head and neck) cancer (TRC); 40% reported that their smoking status was assessed only on their first clinic visit, while 32% were assessed at a few visits and 12% all visits. Most felt smoking status should be assessed at the first visit (95%), while half (58%) felt it should be assessed each visit. Most felt comfortable with being assessed (96%), felt it was important for clinicians to be aware of smoking status (98%) and that smoking cessation discussions should occur at the first visit (87%). Most preferred being assessed by their oncologist (88%); less than half preferred being asked by another healthcare provider (44%), on paper (29%) or e-surveys (32%). Compared to ex/never smokers, current smokers were assessed more often at every/most visits (36% vs 20% P= 0.001) and were less comfortable with being assessed (88% vs 98% P< 0.001). Among current smokers, lung cancer pts were more agreeable (58%) to being assessed each visit compared to head/neck (aOR 2.48 95% CI [0.9-6.5] P= 0.06) and non TRCs (aOR 2.63 [1.0-6.8] P= 0.05). Among all, pts who are older (aOR 1.03 [1.0-1.1]), curative (aOR 1.92 [1.1-3.2]) and smoked less (aOR 0.98 per pkyr [0.97-0.99]) were more agreeable to assessment at each visit. Most pts also felt oncologists should screen for second hand smoke exposure (92%), felt its assessment was important (93%) and should help others who smoke to quit (68%). Conclusions: Most cancer pts felt that assessment of smoking status was important, were comfortable being assessed and preferred being assessed directly by their oncologist. Routine screening of those currently smoking is recommended to help with cessation.
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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 | 0.008 |
| 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 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".