Association between tobacco use, pain expression, and maladaptive coping among patients with advanced cancer.
Bibliographic record
Abstract
65 Background: Cancer patients who smoke have been reported to have higher pain expression and increased risk for opioid abuse. The purpose of our study is to evaluate the association between tobacco use, symptom expression, and maladaptive coping in advanced cancer patients. Methods: We prospectively enrolled advanced cancer patients evaluated in an outpatient Supportive Care Center and collected data on patient demographics, cancer diagnosis, morphine equivalent daily dose (MEDD), cigarette smoking status using Behavioral Risk Factor Surveillance System, symptom expression as measured by Edmonton Symptom Assessment Scale, Cut down/Annoyed/Guilty/Eye opener (CAGE alcoholism questionnaire), short form Screener and Opioid Assessment for Patients with Pain (SOAP-SF) survey, and Brief COPE Questionnaire. Results: Among399 patients, 195 (49%) were never smokers, 158 (40%) former smokers, and 46 (11%) current smokers. The most common malignancies were gastrointestinal (21.1%) and breast (19.5%). Never smokers were more likely to be female (p = 0.005). Current smokers expressed significantly higher pain scores at consultation than former or never smokers [median 7 vs. 6 vs. 5, respectively (p = 0.015)], increased MEDD (median 90 vs. 60 vs. 50, p = 0.002), and more likely to screen CAGE positive (33% vs. 24% vs. 8.7%, p < 0.0001). Compared with former and never smokers, current smokers were significantly more likely to cope with substance use (p = 0.02), denial (p = 0.007), and self-blame (< 0.0001), while both current and former smokers significantly more likely to use venting (p = 0.04). In addition, current smokers compared with former and never smokers were significantly more likely screen positive (≥ 4) on the SOAP-SF survey (74% vs. 13% vs. 9.3%, p = < 0.0001) and clinicians rated patients to be at higher risk for maladaptive coping (6.5% vs 2.5% vs. 1.5%, p = 0.003). Conclusions: In advanced cancer, current and former smokers were significantly more likely to have higher pain expression, CAGE positivity, and increased MEDD at consultation. In addition, a history of current or past tobacco use in advanced cancer patients was associated with increased risk of maladaptive coping.
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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 | 0.000 | 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".