Patterns of use and adverse events reported among persons who regularly inject buprenorphine: a systematic review
Bibliographic record
Abstract
BACKGROUND AND AIMS: Given the ongoing opioid crisis, novel interventions to treat severe opioid use disorder (OUD) are urgently needed. Injectable opioid agonist therapy (iOAT) with diacetylmorphine or hydromorphone is effective for the treatment of severe, treatment-refractory OUD, however barriers to implementation persist. Intravenous buprenorphine for the treatment of OUD (BUP iOAT) has several possible advantages over traditional iOAT, including a safety profile that might enable take-home dosing. We aimed to characterize injecting practices among real-world populations of persons who regularly inject buprenorphine, as well as associated adverse events reported in order to inform a possible future BUP iOAT intervention. METHODS: We conducted a systematic review. We searched MEDLINE, EMBASE, and PsycINFO from inception through July 2020 and used backwards citation screening to search for publications reporting on dose, frequency among persons who regularly inject the drug, or adverse events associated with intravenous use of buprenorphine. The review was limited to English language publications and there was no limitation on study type. Study quality and risk of bias was assessed using the Mixed Methods Appraisal Tool. Narrative synthesis was used in reporting the results. RESULTS: Eighty-eight studies were included in our review. Regular injection of buprenorphine was identified across diverse settings world-wide. Daily dose of oral buprenorphine injected was < 1-12 mg. Frequency of injection was 0-10 times daily. Adverse events could be characterized as known side effects of opioids/buprenorphine or injection-related complications. Most studies were deemed to be of low quality. CONCLUSIONS: Extramedical, intravenous use of buprenorphine, continues to be documented. BUP iOAT may be feasible and results may inform the development of a study to test the efficacy and safety of such an intervention. Future work should also examine acceptability among people with severe OUD in North America. Our review was limited by the quality of included studies.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.006 |
| Bibliometrics | 0.011 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.000 |
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".