Trajectories of opioid consumption as predictors of patient‐reported outcomes among individuals attending multidisciplinary pain treatment clinics
Bibliographic record
Abstract
PURPOSE: This study aimed to identify opioid consumption trajectories among persons living with chronic pain (CP) and put them in relation to patient-reported outcomes 6 months after initiating multidisciplinary pain treatment. METHODS: This study used data from the Quebec Pain Registry (2008-2014) linked to longitudinal Quebec health insurance databases. We included adults diagnosed with CP and covered by the Quebec public prescription drug insurance plan. The daily cumulative opioid doses in the first 6 months after initiating multidisciplinary pain treatment were transformed into morphine milligram equivalents. An individual-centered approach involving principal factor and cluster analyses applied to longitudinal statistical indicators of opioid use was conducted to classify trajectories. Multivariate regression models were applied to evaluate the associations between trajectory group membership and outcomes at 6-month follow-up (pain intensity, pain interference, depression, and physical and mental health-related quality of life). RESULTS: We identified three trajectories of opioid consumption: "no or very low and stable" opioid consumption (n = 2067, 96.3%), "increasing" opioid consumption (n = 40, 1.9%), and "decreasing" opioid consumption (n = 39, 1.8%). Patients in the "no or very low and stable" trajectory were less likely to be current smokers, experience polypharmacy, use opioids or benzodiazepine preceding their first visit, or experience pain interference at treatment initiation. Patients in the "increasing" opioid consumption group had significantly greater depression scores at 6-month compared to patients in the "no or very low and stable" trajectory group. CONCLUSION: Opioid consumption trajectories do not seem to be important determinants of most PROs 6 months after initiating multidisciplinary pain treatment.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | no category Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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, unvalidatedLabeled directly by 2 models reading the full record.
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".