The Impact of Misuse and Diversion of Opioid Substitution Treatment Medicines: Evidence Review and Expert Consensus
Bibliographic record
Abstract
BACKGROUND/AIMS: Opioid substitution treatment (OST) improves outcomes in opioid dependence. However, controlled drugs used in treatment may be misused or diverted, resulting in negative treatment outcomes. This review defines a framework to assess the impact of misuse and diversion. METHODS: A systematic review of published studies of misuse and diversion of OST medicines was completed; this evidence was paired with expert real-world experience to better understand the impact of misuse and diversion on the individual and on society. RESULTS: Direct impact to the individual includes failure to progress in recovery and negative effects on health (overdose, health risks associated with injecting behaviour). Diversion of OST has impacts on a community that is beyond the intended OST recipient. The direct impact includes risk to others (unsupervised use; unintended exposure of children to diverted medication) and drug-related criminal behavior. The indirect impact includes the economic costs of untreated opioid dependence, crime and loss of productivity. CONCLUSION: While treatment for opioid dependence is essential and must be supported, it is vital to reduce misuse and diversion while ensuring the best possible care. Understanding the impact of OST misuse and diversion is key to defining strategies to address these issues.
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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 | 0.011 | 0.032 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.008 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 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".