Effectiveness of long-term opioid therapy among chronic non-cancer pain patients attending multidisciplinary pain treatment clinics: A Quebec Pain Registry study
Why this work is in the frame
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Bibliographic record
Abstract
ABSTRACT Objective The objective of this study was to investigate in a real-life context the effectiveness of long-term opioid therapy for reducing pain intensity and interference and improving health-related quality of life (QOL) in patients with chronic noncancer pain. Methods Participants were 893 patients (age = 52.4 ± 14.1, female = 62.4%) enrolled in the Quebec Pain Registry (2008–2011) who completed questionnaires before their first visit at one of three multidisciplinary pain management clinics and 6 and 12 months thereafter. Based on their opioid use profile (OUP), patients were categorized as nonusers, non-lasting users, or lasting users. Data were analyzed using generalized estimating equations. Results More than 60% of patients newly initiated on opioid therapy stopped their medication mainly because of adverse effects and/or lack of pain relief. OUP significantly predicted pain intensity and interference and physical QOL (pQOL; P values < 0.001). Lasting users of opioids reported higher levels of pain intensity and interference and poorer pQOL than nonusers and/or non-lasting users over the 12-month follow-up (P values < 0.001). However, all effect sizes were small, thus questioning the clinical significance of these group differences. Among lasting users, more than 20% of patients experienced a meaningful amelioration in pain intensity and interference as well as mental QOL (mQOL), whereas only 8% exhibited improved pQOL. Discussion A significant subgroup of patients may benefit from long-term opioid therapy in terms of pain severity and mQOL but the majority do not. The challenge facing clinicians is how to identify who the responders will be.
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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 | 0.011 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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 it