Considerations for Purchasing Drug Checking Technologies: Perspectives from Toronto’s Drug Checking Service
Bibliographic record
Abstract
With the unregulated drug supply-particularly the unregulated opioid supply-becoming increasingly more toxic, more contaminated, and less predictable, drug checking has emerged as an essential public health service: informing individuals who use drugs, as well as those who care and advocate for them, in real-time. For those looking to offer drug checking services in community settings, choosing a technology can be an arduous task. With very little regulatory oversight of drug checking technologies, it can be difficult for organizations that specialize in harm reduction to ascertain what questions to ask drug checking technology vendors to ensure they invest in a technology that best suits the needs of their community. Looking to help those that lack drug checking and technical expertise, Toronto's Drug Checking Service has compiled a list of questions to equip organizations to make informed decisions when it comes to purchasing drug checking technologies. Having developed and operated a drug checking service since 2018, Toronto's Drug Checking Service is uniquely positioned to share its expertise and insights.
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 | 0.007 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.024 | 0.009 |
| Scholarly communication | 0.014 | 0.006 |
| Open science | 0.002 | 0.004 |
| Research integrity | 0.011 | 0.010 |
| Insufficient payload (model declined to judge) | 0.023 | 0.002 |
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".