Evaluating the Impact of Prescribed Versus Nonprescribed Benzodiazepine Use in Methadone Maintenance Therapy: Results From a Population-based Retrospective Cohort Study
Bibliographic record
Abstract
OBJECTIVES: Benzodiazepine (BZD) use is common in patients who are engaged in methadone as a treatment for opioid use disorder. BZD prescribing is generally discouraged for this patient population due to the increased risk of BZD dependence and BZD use disorder, medication-assisted treatment (MAT) discontinuation, and opioid-overdose death. However, some patients have concurrent mental health disorders, where BZD use may be clinically indicated. This study evaluates the impact of prescribed BZD on MAT outcomes. METHODS: Linking urine drug screening data (UDS) and prescribing information from single-payer health records, we conducted a retrospective Kaplan-Meier analysis between patients using prescribed and nonprescribed BZD with methadone treatment retention as the primary outcome. Data are from a network of 52 outpatient clinics in Ontario, Canada, between January 1, 2006 and June 30, 2013. RESULTS: We identified 3692 patients initiating methadone-assisted treatment for the first time; 76% were BZD-/UDS- (no BZD prescription and <30% screens positive for BZD); 13% were BZD+/UDS-; 6% BZD-/UDS+; and 6% BZD+/UDS+. Using 1-year treatment retention as a primary outcome, patients using nonprescribed BZD (BZD-/UDS+) were twice as likely (adjusted odds ratio 0.38, 95% confidence interval 0.27-0.53) to discontinue treatment as those not using BZD (BZD-/UDS-), or those using BZD in a prescribed manner (BZD+/UDS+). CONCLUSIONS: Our findings suggest that prescribed BZD can be used during methadone MAT without impacting a patient's retention in MAT, but nonprescribed BZD use is predictive of treatment discontinuation. Importantly, we urge both the physician and patient to seek alternative clinical options to BZD prescribing, due to the potential for developing physical dependence (and BZD use disorder) to BZD and the risks of negative interactions with opioids.
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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 | 0.004 | 0.009 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".