Methadone to buprenorphine/naloxone induction without withdrawal utilizing transdermal fentanyl bridge in an inpatient setting—Azar method
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Although buprenorphine/naloxone is widely recognized as first-line therapy for opioid use disorder, the requirement for moderate withdrawal prior to initiation in efforts to avoid precipitated withdrawal can be a barrier to its initiation. METHODS: We present a case utilizing transdermal fentanyl as a bridging treatment to eliminate withdrawal during the transition from methadone to buprenorphine/naloxone in a patient who had ongoing significant intravenous heroin use while on methadone. RESULTS: Patient was successfully transitioned from methadone to buprenorphine/naloxone without a period of withdrawal utilizing transdermal fentanyl as a bridge in an inpatient setting. DISCUSSION AND CONCLUSIONS: Our experience indicates a transdermal depot of fentanyl allows for slow release and elimination while buprenorphine doses are introduced during an induction without presence of withdrawal, as quantified by serial clinical opiate withdrawal score. SCIENTIFIC SIGNIFICANCE: This case report highlights ways to minimize barriers to induction of first-line opioid substitution therapy, buprenorphine/naloxone, by eliminating withdrawal during induction phase utilizing a fentanyl bridge within the limitations of a transdermal fentanyl bridge in an inpatient setting. (Am J Addict 2018;XX:1-4).
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".