Feasibility and effectiveness of extended-release buprenorphine (XR-BUP) among correctional populations: a systematic review
Bibliographic record
Abstract
Background: Medications for opioid use disorder (MOUD) reduce risks for overdose among correctional populations. Among other barriers, daily dosing requirements hinder treatment continuity post-release. Extended-release buprenorphine (XR-BUP) may therefore be beneficial. However, limited evidence exists.Objectives: To conduct a systematic review examining the feasibility and effectiveness of XR-BUP among correctional populations.Methods: Searches were carried out in Pubmed, Embase, and PsychINFO in October 2023. Ten studies reporting on feasibility or effectiveness of XR-BUP were included, representing n = 819 total individuals (81.6% male). Data were extracted and narratively reported under the following main outcomes: 1) Feasibility; 2) Effectiveness; and 3) Barriers and Facilitators.Results: Studies were heterogeneous. Correctional populations were two times readier to try XR-BUP compared to non-correctional populations. XR-BUP was feasible and safe, with no diversion, overdoses, or deaths; several negative side effects were reported. Compared to other MOUD, XR-BUP significantly reduced drug use, resulted in similar or higher treatment retention rates, fewer re-incarcerations, and was cost-beneficial, with a lower overall monthly/yearly cost. Barriers to XR-BUP, such as side effects and a fear of needles, as well as facilitators, such as a lowered risk of opioid relapse, were also identified.Conclusion: XR-BUP appears to be a feasible and potentially effective alternative treatment option for correctional populations with OUD. XR-BUP may reduce community release-related risks, such as opioid use and overdose risk, as well as barriers to treatment retention. Efforts to expand access to and uptake of XR-BUP among correctional populations are warranted.
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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 | 0.014 | 0.056 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.008 | 0.009 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".