The Howard Street Method: A Community Pharmacy-led Low Dose Overlap Buprenorphine Initiation Protocol for Individuals Using Fentanyl
Bibliographic record
Abstract
OBJECTIVES: Buprenorphine treatment significantly reduces morbidity and mortality for people with opioid use disorder. Fear of precipitated withdrawal remains a barrier to starting buprenorphine for patients who use synthetic opioids, particularly fentanyl. We aim to evaluate the development and implementation of a buprenorphine low dose overlap initiation (LDOI) protocol in an urban public health community pharmacy. METHODS: We performed a retrospective chart review of patients with nonprescribed fentanyl use (N = 27) to examine clinical outcomes of a buprenorphine LDOI schedule, named the Howard Street Method, dispensed from a community pharmacy in San Francisco from January to December 2020. RESULTS: Twenty-seven patients were prescribed the Howard Street Method. Twenty-six patients picked up the prescription and 14 completed the protocol. Of those who completed the protocol, 11 (79%) reported no symptoms of withdrawal and 3 (21%) reported mild symptoms. Four patients (29%) reported cessation of full opioid agonist use and 10 (71%) reported reduction in their use by the end of the protocol. At 30 days, 12 patients (86%) were retained in care and 10 (71%) continued buprenorphine. At 180 days, 6 patients (43%) were retained in care and 2 (14%) were still receiving buprenorphine treatment. CONCLUSIONS: We found that a LDOI blister-pack protocol based at a community pharmacy was a viable intervention for starting buprenorphine treatment and a promising alternative method for buprenorphine initiation in an underresourced, safety-net population of people using fentanyl.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 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".