Awareness among patients with cancer of the harms of continued smoking.
Bibliographic record
Abstract
179 Background: Smoking cessation is becoming an integral part of cancer survivorship. To help improve survivorship education, clinicians need an understanding of patient knowledge of the harms of continued smoking. Methods: Patients with various cancer subtypes were surveyed with respect to self-awareness of the harms of continued smoking on cancer outcomes. Multivariable logistic regression models assessed factors associated with the level of awareness. Results: Among 985 patients, 23% smoked at diagnosis; 34% quit > 1 year prior to diagnosis; 25% had lung, 30% had head and neck cancers; 77% received curative therapy. Many patients reported being unaware that smoking can negatively impact cancer surgery (65%), radiation (74%), chemo outcomes (64%), treatment efficacy (70%), cancer prognosis (54%) and second primary development (52%). Among all patients, those smoking at diagnosis were significantly more likely to be unaware of radiation (aOR = 1.73, 95% CI [1.16-2.57]) and chemo (aOR = 2.10 [1.17-3.79]) toxicities, cancer prognosis (aOR = 1.63 [1.16-2.29]) and second primary risk (aOR = 1.61 [1.14-2.26]). Those with poorer health status were more likely unaware of effects on prognosis (aOR = 1.56 [1.18-2.08]) and second primaries (aOR = 1.54 [1.14-2.08]). Patients with non-tobacco related cancers (non-TRCs) were more likely unaware smoking impacts cancer surgery (aOR = 1.45 [1.04-2.04]) and radiation (aOR = 1.37 [1.01-1.85]). Among smokers at diagnosis, those with non-TRCs (aOR = 4.00 [1.45-11.11]) were more likely unaware smoking can impact chemo outcomes. Awareness was not associated with stage, second-hand smoke exposure, cessation or interest in a smoking cessation program (SCP), but was associated with patients believing that a SCP would be beneficial to their health (aORs = 2.33-4.35, P < 0.03). Among smokers at diagnosis, believing that a SCP is beneficial (51%) was associated with both interest in an in-patient (aOR = 4.65 [2.15-10.03]) and ambulatory (aOR = 4.08 [2.14-7.79]) SCP. Conclusions: Many cancer patients were unaware of the harms of continued smoking; mainly smokers at diagnosis and those with non-TRCs. Patient education should focus on emphasizing awareness, which may help improve patient interest in smoking 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 | 0.005 |
| 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) | 0.002 | 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".