The current state of knowledge on care for co‐occurring chronic pain and opioid use disorder: A scoping review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Opioid use disorder often co-occurs with chronic pain but assessment and treatment of these co-occurring disorders is complex. This review aims to identify current treatments and delivery models for co-occurring chronic pain and opioid use disorder (OUD) documented in the scientific literature. DESIGN: Scoping review. METHODS: The review was conducted in six databases in June 2022 (no time limit): CINAHL, PsycINFO, Web of Science, Cochrane, PubMed and Embase. The PRISMA-ScR checklist was used to guide reporting. RESULTS: Forty-seven publications addressing the issue of co-occurring chronic pain and OUD management were included. Randomized controlled trials provide evidence for the effectiveness of opioid agonist treatments (OAT) such as methadone or buprenorphine/naloxone, as well as for combining OAT with Mindfulness-Oriented Recovery Enhancement or cognitive behavioural therapy. A number of other pharmacological treatments (opioid and nonopioid), nonpharmacological treatments (e.g. physiotherapy) and service delivery models (e.g. simultaneous treatment of comorbidities, interdisciplinary and interprofessional collaboration) are also underlined. In most cases, authors recommend a combination of strategies to meet patient needs. CONCLUSIONS: The scoping review reveals gaps in evidence-based knowledge to effectively care for co-occurring chronic pain and OUD, but several experts recommend the uptake of known 'best' practices such as integrated treatment of the multiple biopsychosocial dimensions of the co-occurring disorders as well as collaborative interdisciplinary work. CLINICAL RELEVANCE: Improving services is dependent on alleviating barriers such as working in silos, the costs associated with nonpharmacological treatments, and the double stigma associated with pain in people with a substance use disorder.
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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 | 0.021 | 0.105 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.021 | 0.022 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.006 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.001 |
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".