Approaches for Managing Benzodiazepine Dependence Arising From Use of the Adulterated Opioid Supply: A Delphi Technique
Bibliographic record
Abstract
OBJECTIVES: Benzodiazepine adulteration of the unregulated opioid supply presents significant clinical challenges, whereby patients can develop physical dependence to benzodiazepines inadvertently. Currently, clinicians lack evidence to guide care of patients potentially experiencing benzodiazepine withdrawal when ceasing use of unregulated opioids. We used a Delphi technique to build consensus around assessment and management of people at risk for benzodiazepine dependence due to the use of unregulated opioids. METHODS: We administered a Delphi Technique with 12 clinicians (physicians, nurses, nurse practitioners, pharmacists) with expertise in substance use disorders, from a Canadian province with a high prevalence of benzodiazepine-adulterated unregulated opioids. The technique involved 4 rounds of consensus building and resulted in 122 consensus statements related to direct clinical care. RESULTS: Final consensus statements include approaches to risk stratification, diagnosis, and management of benzodiazepine withdrawal secondary to use of benzodiazepine-adulterated opioids. At-risk groups include daily/high-volume opioid users, those who intentionally seek benzodiazepine-contaminated opioids, and those who abruptly cease using unregulated opioids. Common co-opioid and benzodiazepine withdrawal symptoms include anxiety, agitation, gastrointestinal upset, insomnia, and confusion, usually peaking around 72 hours from the time of last use. Experts formed consensus on tracking benzodiazepine withdrawal using vitals, CIWA-B, and treatment response to benzodiazepines administered in inpatient settings. CONCLUSIONS: As benzodiazepines become more prevalent in the unregulated drug supply, there is an urgent need for evidence-based care. Key future priorities should focus on developing evidence-based clinical guidance, creating decision-support tools, and advancing research efforts.
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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 | 0.177 | 0.138 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.009 | 0.009 |
| Scholarly communication | 0.004 | 0.005 |
| Open science | 0.004 | 0.016 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.007 | 0.002 |
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".