Pain Management Strategies for Patients Receiving Extended-Release Buprenorphine for Opioid Use Disorder: A Scoping Review
Bibliographic record
Abstract
Objective: Pain is a significant clinical challenge among patients with opioid use disorder (OUD), and management strategies remain diverse and controversial. This scoping review aimed to describe and evaluate the different types of pharmacologic pain management strategies for patients who are prescribed extended-release buprenorphine (BUP-XR) for OUD and experiencing pain. Methods: The databases Ovid Medline, EMBASE, CINAHL, Web of Science, and PsycInfo were searched from their inception to February 2025 for relevant articles. All articles that discuss the treatment of acute or chronic pain among patients receiving BUP-XR were included. Data on the key outcomes of pain severity, related functioning, patient satisfaction, and adverse events were extracted and study quality was rated independently by the authors. Results: The initial search yielded 980 articles. Of those, 56 were assessed for full-text review and a total of 6 articles met inclusion criteria for the study. The overall strength of the evidence was poor, consisting mainly of case series and case reports. Most studies achieved adequate pain control through the continuation of BUP-XR and the combination of full opioid agonists and non-opioid adjuncts, adjunct use of nonsteroidal anti-inflammatory drugs, conversion to sublingual buprenorphine, or performing surgery at trough serum buprenorphine concentration. No cases of respiratory depression or toxicity were observed. Conclusions: This review confirmed that clear guidelines on how to support pain management in BUP-XR treatment have yet to be identified. The majority of clinicians favored a multimodal analgesic approach combining opioids, non-opioid analgesics, and regional anesthesia. Further studies, including high-quality evidence through randomized controlled trials, are needed to find and evaluate optimal adjunctive medications and define overall strategies.
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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 | 0.006 | 0.022 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.010 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".