Novel adulterants in unregulated opioids and their associations with adverse events
Bibliographic record
Abstract
OBJECTIVE: In recent years, Canada's unregulated drug supply has become permeated by novel adulterants (e.g., fentanyl analogues, benzodiazepines, xylazine). While fentanyl has been shown to be associated with overdose mortality and other non-fatal health outcomes, adverse events (AE) associated with these adulterants remain poorly described. This study seeks to identify whether common adulterants identified through drug checking services are associated with increased prevalence of specific adverse events reportedly experienced by people who use drugs. METHODS: Drug checking samples were analyzed using Fourier-transform infrared spectroscopy and immunoassay strips at harm reduction sites in British Columbia. Self-reported AE (e.g., non-fatal overdose, prolonged sedation, seizures) were recorded from individuals who checked opioids post-consumption. Adjusted prevalence ratios (aPR) and 95% confidence intervals (95% CI) of AE among common adulterants were calculated using generalized linear models with a Poisson distribution, controlled for presence of other adulterants, expected drug, geographic location, and month. RESULTS: Between February 2022 and May 2024, 80,415 samples were analyzed at community sites. Among eligible samples, 36.1% were expected opioids, 42.2% of which were checked post-consumption. AE were noted among 10.7% of post-consumption opioid drug checks. After adjustment, the presence of benzodiazepines in opioid samples was associated with increased prevalence of any AE (aPR 1.97; 95% CI 1.70-2.27), as was the presence of xylazine (aPR 1.50; 95% CI 1.09-2.07). Considering specific AE, benzodiazepines were associated with increased prevalence of overdose (aPR 2.05; 95% CI 1.68-2.51) and prolonged sedation (aPR 3.35; 95% CI 2.54-4.43). CONCLUSION: Non-fatal AE associated with unregulated opioids have been largely undescribed. Our findings report specific AE associated with different adulterants in the unregulated opioid supply. With this information, tailored public health interventions and services focused on these adulterants can be developed.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".