Responding to changes in the unregulated drug supply: the need for a dynamic approach to drug checking technologies
Bibliographic record
Abstract
Drug checking services provide individuals who use drugs with the ability to test samples of their drugs for the presence of highly potent substances. However, there has been recent concern about whether the existing repertoire of point-of-care drug checking technologies, such as immunoassay strips and Fourier-transform infrared spectroscopy (FTIR), are adequate in identifying substances in the unregulated drug supply. Carfentanil and nitazene opioids, substances that are even more potent than fentanyl in vitro, have been found in the unregulated supply in North America and pose a challenge to our existing drug checking strategy. For example, etizolam has recently permeated the unregulated drug supply in North America, and has demonstrated the ability to evade point-of-care drug checking technologies. In response to the incessantly changing nature of the unregulated supply, we argue that drug checking technologies and service delivery models must continuously adapt alongside constantly changing drug markets. We provide two examples of emerging technologies, paper spray-mass spectrometry and surface-enhanced Raman spectroscopy, which address many of the shortcomings of existing technologies. For both technologies, we discuss their feasibility, where they can be offered, their advantages, and how they address gaps in our existing technologies. We contend that these technologies, and other emerging technologies, can be integrated into a future approach to drug checking that flexibly uses different technologies and service delivery methods to adapt to changes in the drug supply.
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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 | 0.006 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".