Emergency Department–initiated Interventions for Patients With Opioid Use Disorder: A Systematic Review
Bibliographic record
Abstract
OBJECTIVES: The opioid crisis has risen dramatically in North America in the new millennium, due to both illegal and prescription opioid use. While emergency departments (EDs) represent a potentially strategic setting for interventions to reduce harm from opioid use disorder (OUD), the absence of a recent synthesis of literature limits implementation and scalability. To fill this gap, we conducted a systematic review of the literature on interventions targeting OUDs initiated in EDs. METHODS: Using an explicit search strategy (PROSPERO), the MEDLINE, CINAHL Complete, EMBASE, and EBM reviews databases were searched from 1980 to October 4, 2019. The gray literature was explored using Google Scholar. Study characteristics were abstracted independently. The methodologic quality and risk of bias were assessed. RESULTS: Twelve of 2,270 studies met the inclusion criteria (two of high quality). In addition to the heterogeneity of the outcome measures used (retention in treatment, opioid consumption, and overdose), brief intervention and buprenorphine initiation (six of 12 studies) were the most documented with mixed effects for the former and positive short-term and confined to single ED sites effects for the latter. CONCLUSION: Emergency departments can be an appropriate setting for initiating opioid agonist treatment, but to be sustained, it likely needs to be coupled with community-based follow-up and support to ensure longer-term retention. The scarcity of high-quality evidence on OUD interventions initiated in emergency settings highlights the need for future research.
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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 | 0.010 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.007 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".