Advances in Paper Spray Mass Spectrometry (PS-MS) for On-Site Harm Reduction Drug Checking and Illicit Supply Surveillance
Bibliographic record
Abstract
Harm reduction drug checking utilizing paper spray mass spectrometry (PS-MS) has been the focus of ongoing research since 2017 and has seen many refinements. The presented work is the result of this research and has led to the public-facing PS-MS use for on-site drug checking in Victoria, BC. Included are the improved methods and approaches required to develop and implement PS-MS as an on-site drug checking technology. Critical details regarding appropriate direct mass spectrometry tune and calibration suites required to avoid isobaric interferences, calibration details, quality control strategies, detailed MS scan approaches to implement rapid drug tests, as well as future work considerations are presented. The PS-MS method presented currently directly quantifies 107 targeted drugs in a two-minute measurement, and can be easily adapted to include additional new targets that appear in the unregulated drug supply that are detected by either low or high resolution PS-MS. The presented methodologies provide a framework to assist those interested in implementing PS-MS to reduce harms from the toxic drug supply, but will have value for those developing rapid, quantitative drug testing for other applications.
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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 | 0.006 | 0.005 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.007 | 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".