Effects of smoking on patients with chronic pain: a propensity-weighted analysis on the Collaborative Health Outcomes Information Registry
Bibliographic record
Abstract
Tobacco smoking is associated with adverse health effects, and its relationship to pain is complex. The longitudinal effect of smoking on patients attending a tertiary pain management center is not well established. Using the Collaborative Health Outcomes Information Registry of patients attending the Stanford Pain Management Center from 2013 to 2017, we conducted a propensity-weighted analysis to determine independent effects of smoking on patients with chronic pain. We adjusted for covariates including age, sex, body mass index, depression and anxiety history, ethnicity, alcohol use, marital status, disability, and education. We compared smokers and nonsmokers on pain intensity, physical function, sleep, and psychological and mood variables using self-reported NIH PROMIS outcomes. We also conducted a linear mixed-model analysis to determine effect of smoking over time. A total of 12,368 patients completed the CHOIR questionnaire of which 8584 patients had complete data for propensity analysis. Smokers at time of pain consultation reported significantly worse pain intensities, pain interference, pain behaviors, physical functioning, fatigue, sleep-related impairment, sleep disturbance, anger, emotional support, depression, and anxiety symptoms than nonsmokers (all P < 0.001). In mixed-model analysis, smokers tended to have worse pain interference, fatigue, sleep-related impairment, anger, emotional support, and depression over time compared with nonsmokers. Patients with chronic pain who smoke have worse pain, functional, sleep, and psychological and mood outcomes compared with nonsmokers. Smoking also has prognostic importance for poor recovery and improvement over time. Further research is needed on tailored therapies to assist people with chronic pain who smoke and to determine an optimal strategy to facilitate 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 | 0.021 | 0.036 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.005 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".