Is fentanyl in everything? Examining the unexpected occurrence of illicit opioids in British Columbia’s drug supply
Bibliographic record
Abstract
BACKGROUND: Illicit opioids, including fentanyl, are linked to unprecedented levels of overdose in Canada and elsewhere. The risks associated with illicit opioids can include high potency, unpredictable concentration and the unexpected presence in other drugs. Within this context, we examine drug checking data to better understand the presence of illicit opioids such as fentanyl in other drugs and possible ways to interpret these results. METHODS: Three years (2021-2023) of data (18,474 samples) from Substance Drug Checking in British Columbia, Canada were examined to investigate the risks associated with the detection of opioids in other drugs such as cocaine and methamphetamine, as well as in other drug categories. Samples were tested by paper spray mass spectrometry (PS-MS), fentanyl test strips and Fourier-Transform infrared spectroscopy (FTIR). We examine the 8889 samples not expected to include fentanyl to confirm; if the expected drug was detected, if unexpected opioids were detected, and when the unexpected opioids are in trace concentration. RESULTS: Unexpected opioids were rarely detected (2%) in other drugs (189 of 8889 samples) with most (61.4%) detected at trace concentration levels. Unexpected opioids are far more likely to be found in samples that did not contain the expected drug than in samples that were confirmed to contain the expected drug. The least common scenario (below 1%) were substances that included the expected drug plus unexpected opioid above trace concentration. These findings raise questions on how to interpret and communicate the detection of fentanyl and related opioids in other drugs. We present three potential interpretations: (1) mistaken and misrepresented samples where the expected drug was never detected, (2) cross contamination when opioids were at trace concentration levels, or (3) adulteration as the least frequent scenario where opioids were detected above trace concentrations in combination with the expected drug. CONCLUSIONS: In a region where fentanyl is associated with extreme rates of overdose, it remains rare to find such opioids in other drugs. However, the risk of fentanyl in other drugs remains an ongoing threat that warrants responses by individuals and public health. We provide possible interpretations to inform such responses. Our data raises questions on how to interpret and communicate the detection of fentanyl and other opioids in other drugs.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".