“Macrodosing” Sublingual Buprenorphine and Extended-release Buprenorphine in a Hospital Setting: 2 Case Reports
Bibliographic record
Abstract
OBJECTIVES: To describe 2 case reports in which high-dose administration of sublingual buprenorphine/naloxone quickly stabilized fentanyl users who presented to the hospital. To discuss how early administration of extended-release buprenorphine, before the patient is discharged, may improve retention rates for outpatient buprenorphine treatment. METHODS: Two case reports of fentanyl users presented to the emergency department at the general hospital in Timmins, Canada are described. They were rapidly stabilized on high-dose sublingual buprenorphine/naloxone and then transitioned within 24 to 36 hours to buprenorphine extended-release subcutaneous injection. RESULTS: In both cases, their withdrawal symptoms quickly resolved, without sedation or precipitated withdrawal. Both patients followed up with the outpatient clinic for another injection of extended-release buprenorphine. CONCLUSIONS: High-dose sublingual buprenorphine/naloxone followed by early administration of extended-release buprenorphine quickly and safely relieved withdrawal symptoms in 2 fentanyl users who presented to the hospital emergency department. This novel approach shows promise in improving treatment retention rates for patients using fentanyl. Further research is required to evaluate the safety and effectiveness of this approach.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.008 | 0.005 |
| Insufficient payload (model declined to judge) | 0.003 | 0.001 |
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".