Bibliographic record
Abstract
What Is the Issue? Opioid agonist therapy (OAT), including methadone and buprenorphine-naloxone, is the primary approach for managing opioid use disorder (OUD) in Canada and has been shown to reduce withdrawal symptoms and opioid use. Recent Canadian clinical guidelines prioritize buprenorphine-naloxone as the first-line treatment for OUD. Buprenorphine-naloxone formulations, available as transmucosal tablets or films, are effective for OUD treatment. However, there is a risk of diversion of the tablet formulation; that is, it can be distributed and used by individuals other than those for whom the drugs were prescribed. Crushing buprenorphine-naloxone tablets before administration has been proposed as a potential strategy to mitigate diversion. What Did We Do? We sought to identify, summarize, and critically appraise available studies, as well as review evidence-based guideline recommendations on the administration of sublingual crushed buprenorphine-naloxone tablets for the treatment of opioid dependency. We searched key resources, including journal citation databases, and conducted a focused internet search for relevant evidence published since 2016. One reviewer screened articles for inclusion based on predefined criteria. What Did We Find? We did not find any evidence regarding the clinical effectiveness and safety of sublingual crushed buprenorphine-naloxone tablets for treating opioid dependency. We did not find any evidence-based guidelines regarding sublingual crushed buprenorphine-naloxone tablets for treating opioid dependency. What Does It Mean? We identified no relevant literature on whether crushing buprenorphine-naloxone tablets effectively prevents misuse and diversion in adults with opioid dependence. As a result, no conclusions could be drawn regarding its effectiveness and safety, and no recommendations could be made. Additional research is needed to assess the impact of crushing buprenorphine-naloxone tablets on efficacy, bioavailability, and adverse effects to determine the clinical implications of this practice.
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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 | 0.040 | 0.182 |
| Meta-epidemiology (narrow) | 0.001 | 0.002 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.011 | 0.010 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.008 | 0.009 |
| Open science | 0.004 | 0.003 |
| Research integrity | 0.006 | 0.003 |
| Insufficient payload (model declined to judge) | 0.011 | 0.002 |
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".