Case report: acute care management of severe opioid withdrawal with IV fentanyl
Bibliographic record
Abstract
BACKGROUND: An increasing number of individuals who use drugs in North America are preferentially consuming fentanyl over other opioids. This has significant consequences on the treatment and management of opioid use disorder (OUD) and its concurrent disorders, especially in acute care if opioid requirements are not met. CASE PRESENTATION: We present a patient with severe OUD and daily injection of fentanyl, admitted to hospital for management of acute physical health issues. Due to high opioid requirements and history of patient-initiated discharge, intravenous fentanyl was administered for treatment of opioid withdrawal, and management of pain, which supported continued hospitalization for acute care treatment and aligned with substance use treatment goals. CONCLUSION: This case demonstrates that intravenous fentanyl for management of OUD in hospital can be a feasible approach to meet opioid requirements and avoid fentanyl withdrawal among patients with severe OUD and daily fentanyl use, thereby promoting adherence to medical treatment and reducing the risk of patient-initiated discharge. There is an urgent need to tailor current treatment strategies for individuals who primarily use fentanyl. Carefully designed research is needed to further explore the use of IV fentanyl for acute care management of severe opioid withdrawal in a hospital setting.
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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 | 0.000 | 0.004 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.007 | 0.006 |
| 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".