Xylazine detected in unregulated opioids and drug administration equipment in Toronto, Canada: clinical and social implications
Bibliographic record
Abstract
BACKGROUND: The North American opioid overdose crisis is driven in large part by the presence of unknown psychoactive adulterants in the dynamic, unregulated drug supply. We herein report the first detection of the psychoactive veterinary compound xylazine in Toronto, the largest urban center in Canada, by the city's drug checking service. METHODS: Toronto's Drug Checking Service launched in October 2019. Between then and February 2021, 2263 samples were submitted for analysis. The service is offered voluntarily at harm reduction agencies that include supervised consumption services. Samples were analyzed using gas chromatography-mass spectrometry or liquid chromatography-high resolution mass spectrometry. Targeted and/or untargeted screens for psychoactive substances were undertaken. RESULTS: In September 2020, xylazine was first detected by Toronto's Drug Checking Service. Among samples analyzed from September 2020 to February 2021 expected to contain fentanyl in isolation (610) or in combination with methamphetamine (16), xylazine was detected in 46 samples (7.2% and 12.5% of samples, respectively). Samples were predominantly drawn from used drug equipment. Three of the samples containing xylazine (6.5%) were associated with an overdose. CONCLUSION: We present the first detection of xylazine in Toronto, North America's fourth-largest metropolitan area. The increased risk of overdose associated with use of xylazine and its detection within our setting highlights the importance of drug checking services in supporting rapid responses to the emergence of potentially harmful adulterants. These data also highlight the clinical challenges presented by the dynamic nature of unregulated drug markets and the concomitant need to establish regulatory structures to reduce their contribution to overdose morbidity and mortality.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".