An overview of forensic drug testing methods and their suitability for harm reduction point-of-care services
Bibliographic record
Abstract
Given the current opioid crisis around the world, harm reduction agencies are seeking to help people who use drugs to do so more safely. Many harm reduction agencies are exploring techniques to test illicit drugs to identify and, where possible, quantify their constituents allowing their users to make informed decisions. While these technologies have been used for years in Europe (Nightlife Empowerment & Well-being Implementation Project, Drug Checking Service: Good Practice Standards; Trans European Drugs Information (TEDI) Workgroup, Factsheet on Drug Checking in Europe, 2011; European Monitoring Centre for Drugs and Drug Addiction, An Inventory of On-site Pill-Testing Interventions in the EU: Fact Files, 2001), they are only now starting to be utilized in this context in North America. The goal of this paper is to describe the most common methods for testing illicit substances and then, based on this broad, encompassing review, recommend the most appropriate methods for testing at point of care.Based on our review, the best methods for point-of-care drug testing are handheld infrared spectroscopy, Raman spectroscopy, and ion mobility spectrometry; mass spectrometry is the current gold standard in forensic drug analysis. It would be prudent for agencies or clinics that can obtain the funding to contact the companies who produce these devices to discuss possible usage in a harm reduction setting. Lower tech options, such as spot/color tests and immunoassays, are limited in their use but affordable and easy to use.
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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 | 0.005 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.009 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.003 |
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".