Association between tobacco use and symptom expression, alcoholism, and illicit drug use in patients with advanced cancer.
Bibliographic record
Abstract
154 Background: Our aim was to determine the association between smoking status and symptom expression, opioid use, alcoholism, and illicit drug use in advanced cancer patients. Methods: We retrospectively reviewed 560 consecutive charts from the outpatient Supportive Care Center and identified 300 advanced cancer patients who completed a comprehensive smoking questionnaire. Data on the Edmonton Symptom Assessment Scale (ESAS), morphine equivalent daily dose (MEDD), CAGE (Cut Down, Annoyed, Guilty, Eye Opener) questionnaire for alcoholism screening, and history of illicit drug use were collected. Results: Among 300 advanced cancer patients, 119 (40%) were never-smokers, 148 (49%) were former smokers, and 33 (11%) were current smokers. Compared with never-smokers, current smokers were more likely to be men (58% vs. 29%, P=0.004), report a higher pain expression (median 7.0 vs. 5.0 by the ESAS, P=0.007), be CAGE positive (≥2 yes response) (42% vs. 3%, P<0.001), and have a history of illicit drug use (33% vs. 3%, P<0.001). Compared with never-smokers, former smokers were more likely to be men (60% vs. 29%, P<0.001), have head and neck cancer or lung cancer (30% vs. 13%, P=0.001), be CAGE positive (21% vs. 3%, P<0.001), and have a history of illicit drug use (16% vs. 3%, P<0.001). Current smokers reported a higher pain expression than former smokers (median 7.0 vs. 6.0 by the ESAS, P=0.01), had higher CAGE positivity (42% vs. 21%, P=0.01) and more frequent illicit drug use (33% vs. 16%, P=0.03). The MEDD and the timing of palliative care referral were not significantly different between current or former smokers compared with never-smokers. However, a higher proportion of current smokers were receiving opioids with MEDD ≥30mg at the time of palliative care consultation compared with never-smokers (70% vs. 52%, P=0.08). Conclusions: Our study suggests that current tobacco use is associated with a higher pain expression. In addition, any history of tobacco use is associated with a history of illicit drug use and alcoholism. Advanced cancer patients who smoked cigarettes may be at an increased risk for chemical coping or stronger opioid utilization and further studies are needed.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.001 | 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".