Acceptability and efficacy of naltrexone for criminal justice‐involved individuals with opioid use disorder: a systematic review and meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Criminal justice-involved individuals carry a disproportionately higher burden of opioid use disorder (OUD) than those not involved with the criminal justice system, and are often unable to access opioid agonist therapies such as methadone and buprenorphine. The opioid receptor antagonist naltrexone (NTX) is effective for the prevention of relapse to OUD and may be more acceptable in criminal justice settings. The objectives of this review were to: (1) provide an overall summary effect across studies for the efficacy and acceptability of oral and injectable NTX for the treatment of OUD among criminal justice-involved individuals and (2) examine systematic variations in study results to explain heterogeneity among study-specific effects. METHODS: Systematic review and meta-analysis of 1045 patients across 11 studies (10 randomized controlled trials, one quasi-experimental study). All available outcomes were pooled using random-effects meta-analysis. Subgroup analyses were conducted for oral and injectable naltrexone; meta-regression analyses were conducted for socio-demographic and study-level characteristics. RESULTS: NTX improved retention in treatment [risk ratio (RR) = 1.31; 95% confidence interval (CI) = 1.05, 1.63], reduced rates of re-incarceration (RR = 0.70, 95% CI = 0.54-0.92), reduced opioid relapse (RR = 0.63, 95% CI = 0.53-0.76) and improved opioid abstinence (RR = 1.38, 95% CI = 1.16-1.65). While NTX was associated with a greater burden of adverse events overall (RR = 1.49, 95% CI = 1.13-1.95), the findings were inconclusive as to whether or not a difference was present for the number of serious adverse events or overdoses. CONCLUSIONS: Naltrexone appears to be efficacious and acceptable for the treatment of opioid use disorder among criminal justice-involved individuals; however, the risk for adverse events must be weighed against the potential benefits.
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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 | 0.034 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.017 | 0.038 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| 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".