Invited Commentary: Drug Checking for Novel Insights Into the Unregulated Drug Supply
Bibliographic record
Abstract
Tobias et al. (Am J Epidemiol. 2022;191 (2):241-247) present a novel analysis of time trends in fentanyl concentrations in the unregulated drug supply in British Columbia, Canada. The preexisting knowledge about unregulated drugs had come from law-enforcement seizures and postmortem toxicology. As both of these data sources are subject to selection bias, large-scale drug-checking programs are poised to be a crucial component of the public health response to the unrelenting increase in overdose in North America. As programs expand, we offer 2 guiding principles. First, the primary purpose of these programs is to deliver timely results to people who use drugs to mitigate health risks. Second, innovation is needed to go beyond criminal justice paradigms in laboratory analysis for a more nuanced understanding of health concerns. We provide examples of the role adulterants play in our understanding of drug harms. We also describe the applications and limitations of common laboratory assays, with implications for epidemiologic surveillance. While the research and direct service teams in British Columbia have taken groundbreaking steps, there is still a need to establish best practices for communicating results to sample donors in an approachable yet nonalarmist tone.
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 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 | 0.007 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.009 |
| 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".