Opioid use disorder in primary care: PEER umbrella systematic review of systematic reviews.
Bibliographic record
Abstract
OBJECTIVE: To summarize the best available evidence regarding various topics related to primary care management of opioid use disorder (OUD). DATA SOURCES: MEDLINE, Cochrane Library, Google, and the references of included studies and relevant guidelines. STUDY SELECTION: Published systematic reviews and newer randomized controlled trials from the past 5 to 10 years that investigated patient-oriented outcomes related to managing OUD in primary care, diagnosis, pharmacotherapies (including buprenorphine, methadone, and naltrexone), tapering strategies, psychosocial interventions, prescribing practices, and management of comorbidities. SYNTHESIS: From 8626 articles, 39 systematic reviews and an additional 26 randomized controlled trials were included. New meta-analyses were performed where possible. One cohort study suggests 1 case-finding tool might be reasonable to assist with diagnosis (positive likelihood ratio of 10.3). Meta-analysis demonstrated that retention in treatment improves when buprenorphine or methadone are used (64% to 73% vs 22% to 39% for control), when OUD is treated in primary care (86% vs 67% in specialty care, risk ratio [RR] of 1.25, 95% CI 1.07 to 1.47), and when counseling is added to pharmacotherapy (74% vs 62% for controls, RR = 1.20, 95% CI 1.06 to 1.36). Retention was also improved with naltrexone (33% vs 25% for controls, RR = 1.35, 95% CI 1.11 to 1.64) and reduced with medication-related contingency management (eg, loss of take-home doses as a punitive measure; 68% vs 77% for no contingency, RR = 0.86, 95% CI 0.76 to 0.99). CONCLUSION: There is reasonable evidence that patients with OUD should be managed in the primary care setting. Diagnostic criteria for OUD remain elusive, with 1 reasonable case-finding tool. Methadone and buprenorphine improve treatment retention, while medication-related contingency methods could worsen retention. Counseling is beneficial when added to pharmacotherapy.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.002 |
| 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".