Case series: Symptom‐inhibited fentanyl induction (SIFI) onto treatment‐dose opioid agonist therapy in a community setting
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Existing opioid agonist therapy (OAT) guidelines are far from sufficient to address rising opioid tolerances and potency of the unregulated opioid market in North America. Inadequate starting doses of OAT are a universally recognized barrier for people who use fentanyl. Our objectives are to present a novel induction protocol called symptom-inhibiting fentanyl induction (SIFI) that uses rapid intravenous fentanyl administration to inhibit symptoms of opioid withdrawal. METHODS: We describe two cases highlighting the potential clinical utility of SIFI. RESULTS: This case series demonstrates two safe and successful transitions onto higher-than-standard doses of methadone and slow-release oral morphine harnessing an emerging approach of SIFI in a community clinic setting. DISCUSSION AND CONCLUSIONS: These results support emerging evidence that SIFI is safe and feasible to meet patients' opioid requirements and facilitate rotation onto OAT. Further studies are needed to increase the generalizability of these findings. SCIENTIFIC SIGNIFICANCE: Safe transitions onto treatment-dose OAT are of heightened clinical importance at a time when fentanyl and high-potency synthetic opioids are now the norm. SIFI is a novel induction method that could address significant gaps in the currently available OAT options in the fentanyl era.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.005 | 0.003 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 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".