Benefits people experiencing opioid use disorder derive from opioid agonist therapies
Bibliographic record
Abstract
Opioid use disorder has become an epidemic over the past 20 years. Contamination of the street sourced drug supply with fentanyl and fentanyl analogues has resulted in a substantial increase in the associated overdose rate. Evidence-based treatments exist; however, much of the evidence supporting their use is based on demonstrations of mortality benefit, abstinence rates, treatment retention and cravings reductions. While these are important outcomes, they do not provide a complete picture of the benefit patients derive from these outcomes. As novel approaches and therapeutic agents are brought into practice, a more thorough understanding of the beneficial outcomes derived from existing therapies is needed both to guide implementation and improve access to therapy. Using the methodology of an integrative review this paper seeks to answer the question: beyond mortality benefit, treatment retention, craving reduction, and abstinence, what beneficial outcomes do people experiencing opioid use disorder derive from opioid agonist therapy? The findings of this review, while limited by the both the quantity and quality of evidence found, suggest that beneficial outcomes of opioid agonist therapy include improved mental and physical health, increased economic participation, reduced criminal activity, and improved quality of life. Associated recommendations for integrating the findings into clinical practice, policy, and research are discussed.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".