Implementing an integrated multi‐technology platform for drug checking: Social, scientific, and technological considerations
Bibliographic record
Abstract
The illicit drug overdose crisis in North America continues to devastate communities with fentanyl detected in the majority of illicit drug overdose deaths. The COVID-19 pandemic has heightened concerns of even greater unpredictability in the drug supplies and unprecedented rates of overdoses. Portable drug-checking technologies are increasingly being integrated within overdose prevention strategies. These emerging responses are raising new questions about which technologies to pursue and what service models can respond to the current risks and contexts. In what has been referred to as the epicenter of the overdose crisis in Canada, a multi-technology platform for drug checking is being piloted in community settings using a suite of chemical analytical methods to provide real-time harm reduction. These include infrared absorption, Raman scattering, gas chromatography with mass spectrometry, and antibody-based test strips. In this Perspective, we illustrate some advantages and challenges of using multiple techniques for the analysis of the same sample, and provide an example of a data analysis and visualization platform that can unify the presentation of the results and enable deeper analysis of the results. We also highlight the implementation of a various service models that co-exist in a research setting, with particular emphasis on the way that drug checking technicians and harm reduction workers interact with service users. Finally, we provide a description of the challenges associated with data interpretation and the communication of results to a diverse audience.
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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 | 0.042 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.004 | 0.004 |
| Scholarly communication | 0.013 | 0.012 |
| Open science | 0.004 | 0.009 |
| Research integrity | 0.006 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".