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 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 | 0.037 | 0.154 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.014 | 0.007 |
| Bibliometrics | 0.030 | 0.029 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.012 | 0.001 |
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".