Predictors of Long‐Term Opioid Effectiveness in Patients With Chronic Non‐Cancer Pain Attending Multidisciplinary Pain Treatment Clinics: A Quebec Pain Registry Study
Bibliographic record
Abstract
OBJECTIVE: This study aimed to identify characteristics of individuals who are most likely to benefit from long-term opioid therapy in terms of reduction in pain severity and improved mental health-related quality of life (mQoL) without considering potential risks. METHODS: This was a retrospective cohort study of 116 patients (age = 51.3 ± 12.5 years, male = 42.2%) enrolled in the Quebec Pain Registry between 2008 and 2011 and who initiated opioid therapy after their first appointment in a multidisciplinary pain clinic and persisted with this treatment for at least 12 months. Clinically significant improvement was defined as a 2-point decrease on the PEG (pain, enjoyment of life, and general activity) Scale of pain severity (scored from 0 to 10) at 12-month follow-up and a 10-point increase on the Short-Form-12 Health Survey version 2 (SF12-v2) Mental Health-Related Quality of Life Summary Scale, which corresponds to 1 standard deviation (SD) of the mean in the general population (mean = 50, SD = 10). RESULTS: Clinically significant reduction in pain severity was observed in 26.7% of patients, while improvement in mQoL was reported by 20.2% of patients on long-term opioid therapy. Older age (odds ratio [OR] = 1.04; 95% confidence interval [CI]: 1.0 to 1.08; P = 0.032) and alcohol or drug problems (OR = 0.26; 95% CI: 0.07 to 0.96; P = 0.044) were weakly associated with pain severity at 12-month follow-up. Baseline higher pain severity (OR = 0.62; 95% CI: 0.43 to 0.91; P = 0.014) and baseline higher mQoL (OR = 0.89; 95% CI: 0.83 to 0.95; P = 0.001) were associated with non-improvement in mQoL. CONCLUSION: The analysis failed to identify clinically meaningful predictors of opioid therapy effectiveness, making it difficult to inform clinicians about which patients with chronic non-cancer pain are most likely to benefit from long-term opioid therapy.
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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 | 0.009 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.000 | 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".