Opioids on Trial: A Systematic Review of Interventions for the Treatment and Prevention of Opioid Overdose
Bibliographic record
Abstract
ABSTRACT Background: Canada is in the midst of an opioid epidemic. In 2016, there were more than 2800 apparent opioid-related deaths. Although improved access to naloxone has saved countless lives, it is unclear if there are other effective pharmacological or nonpharmacological interventions for the treatment and prevention of opioid overdose. In this systematic review, we aim to synthesize published findings on such interventions. Methods: We searched 5 electronic databases for randomized controlled studies using either pharmacological or nonpharmacological interventions to treat or prevent opioid overdose, and subsequently extracted and synthesized data from appropriate studies. Results: Twelve studies met our inclusion criteria. Naloxone, nalmefene, and physostigmine were effective in reversing opioid overdose, whereas naltrexone was effective in preventing opioid overdose. Opioid agonists, including methadone, buprenorphine, and diacetylmorphine, were effective in improving secondary outcomes with variable effects on overdose prevention. No trials using primarily nonpharmacological interventions were identified. Conclusions: In this systematic review, naloxone, nalmefene, and physostigmine emerged as effective in treating opioid overdose, whereas naltrexone showed evidence in preventing opioid overdose. Opioid agonists were found to be effective in improving retention in treatment and in reducing illicit opioid use. Pharmacological interventions play a key role in addressing the opioid epidemic; however, evidence for a multidisciplinary approach involving harm reduction and addressing psychosocial barriers could be the topic of subsequent literature reviews.
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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 | 0.039 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.009 |
| Bibliometrics | 0.006 | 0.007 |
| 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) | 0.007 | 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".