Not just fentanyl: Understanding the complexities of the unregulated opioid supply through results from a drug checking service in British Columbia, Canada
Bibliographic record
Abstract
INTRODUCTION: This study examines illicit opioid samples submitted to a drug checking service in British Columbia, Canada. By employing a method capable of identifying and quantifying compounds at low concentrations, the analysis focused on identifying trends in notable compounds such as fentanyl, its analogues, and benzodiazepines. The findings aim to address gaps in supply monitoring and inform public health and drug policies. METHODS: Opioid samples were collected and analyzed over three years using fentanyl and benzodiazepine test strips, Fourier-transform infrared (FTIR) spectroscopy and Paper-Spray Mass Spectrometry (PS-MS). PS-MS was employed to conduct trace-level analysis, provide targeted composition results, and quantify notable ingredients within the samples. The concentrations of fentanyl and benzodiazepines, among other components, were examined. RESULTS: The dataset includes 8122 opioid samples analyzed from January 2021 to December 2023. Analysis revealed that heroin was replaced by fentanyl and its analogues in the opioid supply, as heroin was detected in only 4 % of opioid samples while fentanyl and analogues were detected in 88 %. Fluorofentanyl was found in 70 % of opioid samples, occasionally in combination with fentanyl. Benzodiazepines and their analogues were detected in 49 % of opioid samples, with a notable shift from etizolam to bromazolam. The median fentanyl concentration was 10.6 % (weight/weight), ranging from less than 0.1 % to over 80 %. The median bromazolam concentration was 3.2 %, with a range of less than 0.1 % to over 25 %. CONCLUSION: The study highlights the volatility in the supply and mentions the necessity for a safer opioid supply and robust drug checking methodologies to address the challenges posed by the heterogenous market.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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".