Chronic pain and opioid misuse: a review of reviews
Bibliographic record
Abstract
OBJECTIVE: The crisis of prescription opioid (PO) related harms has focused attention toward identifying and treating high-risk populations. This review aims to synthesize systematic reviews on the epidemiology and clinical management of comorbid chronic pain and PO or other substance misuse. METHODS: A systematic database search was conducted to identify systematic reviews published between 2000 and 2016. Eligible studies were systematic reviews related to chronic non-cancer pain and PO or other substance misuse. Evidence from the included reviews was synthesized according to epidemiology and clinical management themes. RESULTS: Of 1908 identified articles, 18 systematic reviews were eligible for final inclusion. Two meta-analyses estimated the prevalence of chronic non-cancer pain in individuals using POs non-medically to be approximately 48% to 60%, which is substantially higher than the prevalence of chronic non-cancer pain in general population samples (11% to 19%). Five systematic reviews estimated the rates of PO or other opioid use in chronic pain populations with substantial variation in results (0.05% to 81%), likely due to widely varying definitions of dependence, substance use disorder, misuse, addiction, and abuse. Several clinical assessment and treatment approaches were identified, including: standardized assessment instruments; urine drug testing; medication counts; prescription drug monitoring programs; blood level monitoring; treatment agreements; opioid selection; dosing and dispensing strategies; and opioid agonist treatment. However, the reviews commonly noted serious limitations, inconsistencies, and imprecision of studies, and a lack of evidence on effectiveness or clinical utility for the majority of these strategies. CONCLUSION: Overall, current systematic reviews have found a lack of high-quality evidence or consistent findings on the prevalence, risk factors, and optimal clinical assessment and treatment approaches related to concurrent chronic pain and substance misuse. Given the role of systematic reviews in guiding evidence-based medicine and health policy, there is an urgent need for high-quality primary research to guide future systematic reviews to address the escalating epidemic of harms related to chronic pain and substance misuse.
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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 | 0.035 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.007 |
| Bibliometrics | 0.013 | 0.015 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".