A strategy for the detection of benzodiazepine drugs using low‐resolution paper‐spray mass spectrometry for harm reduction drug checking
Bibliographic record
Abstract
The ability to detect newly emerging substances is of great importance in reducing harms for people who use drugs. New psychoactive substances including novel benzodiazepines in the illicit drug supply have been linked to high rates of overdose deaths while complicating drug checking as an overdose prevention strategy. Paper-spray mass spectrometry (PS-MS) has emerged as a novel strategy to rapidly detect trace components in street drug samples. While targeted, low-resolution PS-MS methods have proven effective, newly emerging substances are often missed. To address this, a method was applied to low-resolution full-scan PS-MS data to aid in the early detection and identification of novel benzodiazepines in the unregulated drug supply. Using the developed method, true positives rates of 0.89 and 0.75 were achieved for bromazolam and etizolam in street samples obtained in a community drug checking service. The applicability of the method was further demonstrated for a novel benzodiazepine, desalkylgidazepam, that has recently emerged in the illicit drug supply.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".