The Impact of Benzodiazepine Use on Methadone Maintenance Treatment Outcomes
Bibliographic record
Abstract
The purposes of this study were to examine predictors of benzodiazepine use among methadone maintenance treatment patients, to determine whether baseline benzodiazepine use influenced ongoing use during methadone maintenance treatment, and to assess the effect of ongoing benzodiazepine use on treatment outcomes (i.e., opioid and cocaine use and treatment retention). A retrospective chart review of 172 methadone maintenance treatment patients (mean age = 34.6 years; standard deviation = 8.5 years; 64% male) from January 1997 to December 1999 was conducted. At baseline, 29% were "non-users" (past year) of benzodiazepine, 36% were "occasional users," and 35% were "regular/problem users." Regular/problem users were more likely to have started opioid use with prescription opioids, experienced more overdoses, and reported psychiatric comorbidity. Being female, more years of opioid use, and a history of psychiatric treatment were significant predictors of baseline benzodiazepine use. Ongoing benzodiazepine users were more likely to have opioid-positive and cocaine-positive urine screens during methadone maintenance treatment. Only ongoing cocaine use was negatively related to retention. Benzodiazepine use by methadone maintenance treatment patients is associated with a more complex clinical picture and may negatively influence treatment outcomes.
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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 | 0.001 | 0.012 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".