Fentanyl assisted treatment: a possible role in the opioid overdose epidemic?
Bibliographic record
Abstract
BACKGROUND: The current opioid overdose epidemic affecting communities across North America is increasingly driven by illicitly manufactured fentanyl and its related analogues. A variety of public health interventions have been implemented and scaled up, including opioid agonist treatments (OAT). While these treatments are successful for many individuals, they have a variety of limitations. It is critical to trial alternative treatments if conventional opioid agonist treatment options are not successful for a proportion of patients who use illicit fentanyl. MAIN BODY: Prescription fentanyl has been widely used for pain management. The use of transdermal fentanyl, specifically, which provides long acting and stable drug levels post-titration over several days, should be explored as an opioid agonist treatment option. The use of transdermal fentanyl for patients who use illicit fentanyl is currently being piloted in Vancouver, Canada. To address potential diversion, the patch is signed, dated, and a film dressing is applied to mitigate tampering. Evaluation outcomes are still pending, but there have been no adverse outcomes thus far and clinical improvements have been noted for many patients. This exploratory therapeutic approach should be considered across multiple settings and rigorously evaluated. CONCLUSIONS: There are known limitations to existing OAT options and there is a need to urgently evaluate alternative strategies for patients who are using illicit fentanyl not successfully treated with conventional OAT. Many patients may be attracted to, and retained in, fentanyl assisted treatment. This may be another strategy for certain patients to prevent harms caused by illicit fentanyl use, including overdose and death.
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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 | 0.004 | 0.019 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.003 | 0.006 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.004 | 0.005 |
| Insufficient payload (model declined to judge) | 0.025 | 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".