Navigating new norms: Addiction specialists' perspectives on opioid use disorder treatments and policy challenges in the fentanyl era
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Amidst increasing opioid-related overdoses in the USA, opioid use disorder (OUD) treatment has seen few novel treatments emerge. High-potency synthetic opioids (HPSOs) have altered clinical approaches, prompting evaluation of existing medications for opioid use disorder (MOUD) and interest in slow-release oral morphine (SROM) as another therapeutic option. Here we survey addiction specialists on the influence of HPSOs on clinical practice, views on current MOUD regulations, and openness to novel therapies such as SROM. METHODS: tests and Fisher's exact tests to compare respondent characteristics. RESULTS: Approximately 89% of respondents (N = 91) acknowledge that HPSOs shifted addiction treatment in recent years, with 86% modifying their MOUD prescribing accordingly. Moreover, 84% report having patients who could benefit from other full opioid agonists beyond methadone for OUD management. Many report off-label prescribing of full agonist opioids other than methadone for withdrawal symptoms or initiating MOUD. Eighty percent reported being in favor of incorporating SROM as a third-line monotherapy for OUD. DISCUSSION AND CONCLUSION: This sample of addiction specialists supports innovative alternatives for MOUD in the USA to combat the challenges posed by fentanyl and related HPSOs. Future work should further addiction specialists' opinions on barriers to OUD treatment and exploration of these international strategies in the USA. SCIENTIFIC SIGNIFICANCE: This appears to be the first study exploring addiction specialists' perspectives on regulatory barriers to OUD treatment and their willingness to uptake internationally adopted strategies such as SROM.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.023 | 0.029 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.012 | 0.013 |
| Scholarly communication | 0.010 | 0.008 |
| Open science | 0.001 | 0.008 |
| Research integrity | 0.007 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".