Detection of synthetic cannabinoid adulteration in the unregulated drug supply in three Canadian settings
Bibliographic record
Abstract
INTRODUCTION: Novel psychoactive substances (NPS) are increasingly being consumed worldwide, with synthetic cannabinoids and synthetic opioids being the second and third most commonly used NPS, respectively. Certain synthetic cannabinoids can produce significant harms, particularly when used with opioids. The objective of this study was to characterise the presence of synthetic cannabinoids in the unregulated drug supply in three Canadian settings METHODS: In the British Columbia setting, all samples were first analysed at point-of-care using combination Fourier-transform infrared (FTIR) spectroscopy and fentanyl immunoassay strips prior to confirmatory testing using quantitative nuclear magnetic resonance spectroscopy, gas chromatography/mass spectrometry (GC/MS) and/or liquid chromatography/mass spectrometry (LC/MS). In the Toronto, Ontario setting, the samples were analysed directly by GC/MS, LC/MS liquid chromatography-high resolution/mass spectrometry. RESULTS: Between January 2018 and December 2019, 38 (2.8%) synthetic cannabinoid samples were detected in the unregulated drug supply (25/909 in British Columbia and 13/440 in Ontario). In British Columbia and Ontario, 76% and 85% of samples, respectively, were expected by individuals to be an opioid. Synthetic cannabinoids detected included AMB-FUBINACA, AB-FUBINACA, 5-fluoro-MDMB-PINACA, and 5-fluoro-MDMB-PICA, and largely co-occurred with fentanyl. In the British Columbia context, Fourier-transform infrared spectroscopy failed to detect synthetic cannabinoid compounds in almost half (48%) of the samples at point-of-care. DISCUSSION AND CONCLUSIONS: As point-of-care technologies failed to detect these compounds in many occasions, our findings demonstrate the importance of laboratory confirmatory analysis to identify NPS. Given the high risk of harm associated with the consumption of synthetic cannabinoids, further research should investigate the reasons for adulteration.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.004 | 0.001 |
| 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".