Does the Addition of Naloxone in Buprenorphine/Naloxone Affect Retention in Treatment in Opioid Replacement Therapy?
Bibliographic record
Abstract
BACKGROUND: Opioid maintenance therapy is an evidence-based first-line treatment approach to reduce the problems associated with opioid use disorders. Buprenorphine and methadone are the two most commonly recommended pharmacotherapies. Individuals who remain in treatment longer tend to have a reduced drug use, a higher social functioning, and a higher quality of life. The addition of naloxone to buprenorphine (bup/nx) was developed, in part, to help increase retention in treatment. However, this has not been shown in research. The objective of this review was to examine whether bup/nx is more effective than buprenorphine and methadone, to ultimately determine whether the addition of naloxone shows a clinical difference. METHODS: The literature search was conducted using the electronic databases PubMed, Embase, and Cochrane. Search strategies were thoroughly developed and modified for each database by combining relevant MeSH and Emtree terms as well as keywords such as "bup/nx," "buprenorphine," and "naloxone." The outcome measure was treatment retention, as determined by the number of days a participant remains in a treatment program. RESULTS: There were four studies included in the review. The data were analyzed with Review Manager software. There was no statistically significant result for bup/nx compared with methadone or buprenorphine. CONCLUSION: Bup/nx may be an alternative to standard treatments such as buprenorphine and methadone as the addition of naloxone does not affect retention in treatment.
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| 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".