Risk of Opioid Abuse and Biopsychosocial Characteristics Associated With This Risk Among Chronic Pain Patients Attending a Multidisciplinary Pain Treatment Facility
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study were to (1) determine the proportion of patients referred to a multidisciplinary pain treatment facility at risk of opioid abuse, (2) examine biopsychosocial factors associated with this risk, and (3) compare patient outcomes 6 months later across risk of opioid abuse and type of treatment (opioids vs. no opioids). METHODS: Participants were 3040 patients (mean age=53.3±14.7 y; female=56%) enrolled in the Quebec Pain Registry between July 2012 and May 2014. Patients answered self-report and nurse-administered questionnaires (pain and psychosocial constructs, Opioid Risk Tool, pain medication, etc.) before initiating treatment at the multidisciplinary pain treatment facility and 6 months later. Data were analyzed using the Pearson χ tests, multivariable binary logistic regression, and multivariate general linear model. RESULTS: Results showed that 81%, 13%, and 6% of patients were at low, moderate, and severe risk of opioid abuse, respectively. Civil status, pain duration, mental health-related quality of life, and cigarette smoking were significantly associated with risk of opioid abuse (P<0.001). There was a significant interaction between risk of opioid abuse and type of treatment in predicting 6-month pain outcomes and quality of life. DISCUSSION: Almost 20% of patients had a moderate/severe risk of opioid abuse; whether these patients were taking opioids or not for their pain, they had worse outcomes at follow-up. These results point to the importance of assessing risk of opioid abuse in chronic pain patients and to consider how this risk may impact on their clinical evolution.
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 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 | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.003 | 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".