Caring for Hospitalized Adults With Opioid Use Disorder in the Era of Fentanyl
Bibliographic record
Abstract
Importance: The rise of fentanyl and other high-potency synthetic opioids across US and Canada has been associated with increasing hospitalizations and unprecedented overdose deaths. Hospitalization is a critical touchpoint to engage patients and offer life-saving opioid use disorder (OUD) care when admitted for OUD or other medical conditions. Observations: Clinical best practices include managing acute withdrawal and pain, initiating medication for OUD, integrating harm reduction principles and practices, addressing in-hospital substance use, and supporting hospital-to-community care transitions. Fentanyl complicates hospital OUD care. Fentanyl's high potency intensifies pain, withdrawal, and cravings and increases the risk for overdose and other harms. Fentanyl's unique pharmacology has rendered traditional techniques for managing opioid withdrawal and initiating buprenorphine and methadone inadequate for some patients, necessitating novel strategies. Further, co-use of opioids with stimulants drugs is common, and the opioid supply is unpredictable and can be contaminated with benzodiazepines, xylazine, and other substances. To address these challenges, clinicians are increasingly relying on emerging practices, such as low-dose buprenorphine initiation with opioid continuation, rapid methadone titration, and the use of alternative opioid agonists. Hospitals must also reconsider conventional approaches to in-hospital substance use and expand clinicians' understanding and embrace of harm reduction, which is a philosophy and set of practical strategies that supports people who use drugs to be safer and healthier without judgment, coercion, or discrimination. Hospital-to-community care transitions should ensure uninterrupted access to OUD care after discharge, which requires special consideration and coordination. Finally, improving hospital-based addiction care requires dedicated infrastructure and expertise. Preparing hospitals across the US and Canada to deliver OUD best practices requires investments in clinical champions, staff education, leadership commitment, community partnerships, quality metrics, and financing. Conclusions and Relevance: The findings of this review indicate that fentanyl creates increased urgency and new challenges for hospital OUD care. Hospital clinicians and systems have a central role in addressing the current drug crisis.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.004 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.004 |
| Research integrity | 0.002 | 0.007 |
| Insufficient payload (model declined to judge) | 0.007 | 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".