Superiority of Buprenorphine over Suboxone in Preventing Addiction Relapse in Opioid Addicts under Maintenance Therapy: A Double-Blind Clinical Trial
Bibliographic record
Abstract
Background: In maintenance therapy for opioid addiction, to reduce the risk of buprenorphine (BUP) abuse, the combination of BUP and naloxone (NX) has been developed and is commercially available as suboxone (BUP/NX). This study was designed to compare addiction relapse frequency in patients receiving BUP and BUP/NX as maintenance therapy. Methods: In this double-blind clinical trial with cross over design, 100 opioid abusers were randomly assigned to two treatment groups to receive either BUP or BUP/NX. After three months, without a time-out period, subjects undertook treatment with the other drug. The subjects were screened weekly for urinary morphine. Results: In each of the study arms, when the patients were given BUP/NX, the number of relapses was significantly higher compared to when they received BUP (0.13±0.24 vs. 0.04±0.09, P = 0.001). If participants’ age was taken into account, the number of relapses was significantly higher when BUP/NX was given in age groups of 31 to 40 years and over 50 years (P < 0.05). The length of addiction had also a significant impact on the number of relapses, i.e., patients with over 10-year history of addiction had higher number of relapses if they were given BUP/NX compared with BUP (P < 0.05). Conclusion:BUP seems to be more effective than BUP/NX in preventing addiction relapse in opioid abusers under maintenance treatment.
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 | 0.004 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.005 | 0.002 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".