Impact of Chronic Pain on Treatment Prognosis for Patients with Opioid Use Disorder: A Systematic Review and Meta-analysis
Bibliographic record
Abstract
BACKGROUND: While a number of pharmacological interventions exist for the treatment of opioid use disorder, evidence evaluating the effect of pain on substance use behavior, attrition rate, and physical or mental health among these therapies has not been well established. We aim to evaluate these effects using evidence gathered from a systematic review of studies evaluating chronic non-cancer pain (CNCP) in patients with opioid use disorder. METHODS: We searched the Medline, EMBASE, PubMed, PsycINFO, Web of Science, Cochrane Database of Systematic Reviews, ProQuest Dissertations and theses Database, Cochrane Central Register of Controlled Trials, World Health Organization International Clinical Trials Registry Platform Search Portal, and National Institutes for Health Clinical Trials Registry databases to identify articles evaluating the impact of pain on addiction treatment outcomes for patients maintained on opioid agonist therapy. RESULTS: Upon screening 3,540 articles, 14 studies with a combined sample of 3,128 patients fulfilled the review inclusion criteria. Results from the meta-analysis suggest that pain has no effect on illicit opioid consumption [pooled odds ratio (pOR): 0.70, 95%CI 0.41-1.17; I (2) = 0.0] but a protective effect for reducing illicit non-opioid substance use (pOR: 0.57, 95%CI 0.41-0.79; I (2) = 0.0). Studies evaluating illicit opioid consumption using other measures demonstrate pain to increase the risk for opioid abuse. Pain is significantly associated with the presence of psychiatric disorders (pOR: 2.18; 95%CI 1.6, 2.9; I (2) = 0.0%). CONCLUSION: CNCP may increase risk for continued opioid abuse and poor psychiatric functioning. Qualitative synthesis of the findings suggests that major methodological differences in the design and measurement of pain and treatment response outcomes are likely impacting the effect estimates.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.003 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".