Effectiveness of long-term opioid therapy among chronic non-cancer pain patients attending multidisciplinary pain treatment clinics: A Quebec Pain Registry study
Bibliographic record
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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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".