Factors Associated with Pain Treatment Satisfaction Among Patients with Chronic Non-Cancer Pain and Substance Use
Bibliographic record
Abstract
INTRODUCTION: A better understanding of pain treatment satisfaction in patients with chronic noncancer pain (CNCP) and substance use is needed, especially as opioid prescribing policies are changing. We sought to identify factors associated with pain treatment satisfaction in individuals with CNCP on recent opioid therapy and prior or active substance use. METHODS: An exploratory cross-sectional analysis using baseline data from a cohort study of 300 adults with CNCP receiving >20 morphine milligram equivalents of opioids for ≥3 of the preceding 12 months and prior or active substance use. Participants completed interviews, clinical assessments, urine drug screening, and medical chart review. RESULTS: Participants were predominantly middle-aged (mean age 57.5 years), Black (44%), and cisgender men (60%). One-third (33%) had high, 28% moderate, and 39% low pain treatment satisfaction. Post-traumatic stress disorder (PTSD), tobacco use, past-year opioid discontinuation, and higher average pain scores were associated with lower satisfaction. HIV and prescription cannabis use were associated with higher satisfaction. CONCLUSIONS: The relationship between PTSD and tobacco use with lower satisfaction should be explored to augment pain outcomes. Higher satisfaction among individuals with HIV and prescription cannabis use presents potential research areas to guide CNCP management and reduce reliance on opioid therapies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".