Fentanyl, carfentanil and other fentanyl analogues in Canada’s illicit opioid supply: A cross-sectional study
Why this work is in the frame
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Bibliographic record
Abstract
Background: Despite the increase in fentanyl-involved overdose deaths in Canada, there have been no national-level studies evaluating the proportion of illicit opioids containing fentanyl or fentanyl analogues in Canada. Methods: This cross-sectional exploratory study characterized trends in fentanyl, carfentanil and other fentanyl analogues within opioids seized by law enforcement agencies in Canada from 2012 to 2022 and submitted to the Health Canada Drug Analysis Service (DAS). Analyses were stratified by province/region. Mann-Kandell tests were used to test for trends. Results: A total of 157,616 samples containing any opioid ("opioid-containing samples") were submitted to the DAS from Canadian provinces between 2012 and 2022, of which 81,165 (51.5%) contained fentanyl or a fentanyl analogue. The percentage of opioid-containing samples that were positive for fentanyl or a fentanyl analogue increased from 3.0% (95% CI: 2.6-3.4%) in 2012-68.3% (67.7-68.9%) in 2022 (p < 0.001 for trend). The percentage of opioid-containing samples that were positive for fentanyl or a fentanyl analogue increased between 2012 and 2022 in all regions. In 2022, the percentage of samples containing fentanyl or an analogue followed an east-to-west gradient: 15.8% (13.3-18.6%) of samples in Atlantic Canada and 84.7% (83.6-85.7%) in British Columbia. Carfentanil was present in 4.9% (4.6-5.2%) of opioid-containing samples in Canada in 2022 and 19.7% (18.3-21.2%) of opioid-containing samples in Alberta. Conclusions: The illicit opioid supply in Canada increasingly contains toxic synthetic opioids. As of 2022, important regional differences existed in the illicit opioid supply in Canada.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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 it