Canadian Drug Notification System on New and Potentially Harmful Substances
Bibliographic record
Abstract
Introduction In response to Canada’s overdose crisis, the Health Canada Drug Analysis Service (DAS) mandate was expanded to support intelligence gathering on illicit drugs. DAS is a unique data source since it is the only Canadian laboratory accredited to analyze drugs seized by all law enforcement agencies from 1988 to present. In 2024, DAS made the National Drug Notification System, identifying new and potentially harmful psychoactive substances, available online, thereby making this information available to the larger scientific community. Methods Using DAS data, the system is articulated around operational definitions of new substance of concern, new mixture, and new form (i.e., stamp, shape, colour, powdery substance or tablets). An automated procedure generates a list of daily warnings that are validated by laboratory experts. Results In 2024, 79 drug notifications were shared with partners, through the system webpage and targeted communications. A total of 17 new psychoactive substances and new precursors were identified for the first time in Canada by DAS. Conclusions The development and full implementation of the system took place over several years and is the most timely tool available to document emergence of new substances in Canada’s illicit drug market.
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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 | 0.004 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.012 | 0.008 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.004 | 0.001 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.048 | 0.013 |
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".