Examining fentanyl and its analogues in the unregulated drug supply of British Columbia, Canada using drug checking technologies
Bibliographic record
Abstract
INTRODUCTION: The emergence of fentanyl and its analogues have contributed to a drastic rise in overdose-related mortality in recent years. The objective of this study was to determine the number of drug checking samples containing fentanyl and fentanyl analogues using both point of care and confirmatory drug checking technologies. METHODS: Point-of-care drug checking data, using a combination of fentanyl immunoassay strips and Fourier-transform infrared spectroscopy (FTIR), were collected at harm reduction sites in Vancouver and Surrey, British Columbia. Based on current recommendations from the British Columbia Centre on Substance Use Drug Checking Project, a subset of these samples was sent for confirmatory analysis using quantitative nuclear resonance spectroscopy, gas chromatography-mass spectrometry and/or liquid chromatography-mass spectrometry. RESULTS: A total of 22,916 samples were tested using FTIR and fentanyl immunoassay strips, of which 6125 (29%) were positive for fentanyl and/or fentanyl analogues. FTIR identified a fentanyl analogue in five samples (all carfentanil). Of the 1467 samples sent for confirmatory analysis, fentanyl was identified in 855 (58%) and fentanyl analogues in 85 (6%), including: carfentanil (n = 56), acetyl fentanyl (n = 15), furanyl fentanyl (n = 9) and cyclopropyl fentanyl (n = 5). DISCUSSION AND CONCLUSION: Our research found that FTIR does not consistently distinguish between fentanyl and its analogues at point of care and that highly sensitive confirmatory drug checking technologies are needed to identify fentanyl analogues. These findings underscore the limitations of current drug checking technologies and the importance of using both point of care and confirmatory drug checking initiatives for monitoring changes in the drug supply.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.002 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".