Factors to guide the use of extended-release buprenorphine formulations for specific patient populations
Bibliographic record
Abstract
Background Opioid agonist therapy is first line in the management of opioid use disorder (OUD). Many guidelines support buprenorphine as the optimal treatment due to its preferred safety profile. Extended-release buprenorphine (BUP-XR) is a weekly or monthly subcutaneously administered formulation of buprenorphine. Emerging research has explored the use of BUP-XR in a variety of patient populations to guide clinicians in determining which patients may or may not benefit from BUP-XR.Methods We conducted a literature review in Medline, PsycInfo and Embase to capture the literature to date on BUP-XR. Abstracts were reviewed, and papers which guided selection of individuals who would or would not benefit from BUP-XR were included.Results Our search returned 359 articles, and 31 met inclusion criteria. Special populations who may benefit from BUP-XR include individuals experiencing homelessness, incarceration, and medical or psychiatric comorbidity, while caution should be used around pregnancy. Patient preference and patient-centered outcomes were recurring considerations to guide the selection of BUP-XR.Conclusions Extended-release buprenorphine formulations represent a relatively new tool for managing OUD and may specifically benefit patients undergoing transitions in care. Further research on the comparative effectiveness of extended-release versus daily-dosing formulations and in other populations of interest is needed.
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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 | 0.047 | 0.245 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.004 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".