Opioid analgesics for chronic noncancer pain in patients prescribed opioid agonist therapy or with opioid use disorder: A systematic review
Bibliographic record
Abstract
Background: Opioid use disorder (OUD) is a growing public health concern in North America, often coexisting with chronic noncancer pain (CNCP). Managing both conditions presents unique challenges, highlighting the need for evidence to guide decision making. Aim: The study aimed to conduct a systematic review that summarizes evidence on the efficacy, effectiveness, and safety of opioid analgesics alone or in combination with opioid agonist therapy (OAT) to manage CNCP in people with OUD or with a history of OUD. Methods: We searched MEDLINE, Embase, PsycINFO, CINAHL and AMED from inception to July 2023 for randomized studies and up to January 2025 for non-randomized studies that explored the efficacy, effectiveness, and safety of opioids for people living with chronic pain and current or prior OUD. We assessed the risk of bias in included studies, evaluated the quality of evidence using the GRADE approach, and provided a narrative summary of treatment effects. Results: Our search identified 15,988 unique citations, of which six observational studies were deemed eligible to inform safety outcomes for review, while no observational studies or RCTs met the eligibility criteria for efficacy or effectiveness outcomes. The likelihood of suicidality was twice as high in CNCP patients with OUD receiving long-term opioid analgesics compared to those without OUD (absolute risk increase: 127; 95% CI: 36 to 249 more participants with suicidality in 1,000 participants; moderate certainty evidence). Compared to opioid analgesics alone, the risk of fatal opioid-related overdose may decrease in patients with CNCP and OUD who receive both opioid analgesics and OAT (absolute risk reduction: 60; 95%CI: 18 to 94 fewer deaths in 1,000 participants; low certainty evidence). Conclusions: There is a paucity of evidence to inform practice and policy regarding opioid analgesic prescribing amongst people with OUD. Existing evidence suggests that such prescribing is associated with a higher risk of suicidality, while the use of OAT together with opioid analgesics in this population may be protective against fatal overdose. Further observational and trial research is needed to clarify the benefits and harms of opioid analgesics for CNCP patients with OUD.
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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 | 0.003 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".