Fentanyl, carfentanil and other fentanyl analogues in Canada’s illicit opioid supply: A cross-sectional study
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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.002 | 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".