Paper spray mass spectrometry: A new drug checking tool for harm reduction in the opioid overdose crisis
Bibliographic record
Abstract
Fentanyl and related psychoactive substances are at the forefront of the opioid overdose crisis, for which a complete solution is not immediately obvious. Drug testing for harm reduction may be an effective approach to both reduce overdoses and importantly, engage people who use drugs (PWUD) with the medical system. Paper spray mass spectrometry (PS-MS) is an ambient ionization strategy that is uniquely suited to address this complicated analytical task. This perspectives article presents the merits of PS-MS, with a focus upon the current state of its use as a candidate drug checking strategy for harm reduction. PS-MS is inherently sensitive and selective, with detection limits in the picogram range. It requires small drug samples (~1 mg) for quantitative drug testing, critical to encourage pre-consumption measurements by PWUD in the context of a harm reduction strategy. Calibrations obtained in surrogate drug matrices containing highly concentrated primary drugs demonstrate comparable sensitivities, a wide calibration range, and minimal matrix effects. PS-MS can be interfaced with high-resolution MS for non-targeted analysis, allowing the identification of novel psychoactive substances as they appear in street drugs. Individual quantitative PS-MS measurements for drug testing can be done in 1 minute or less, resulting in high sample throughput. Significant advancement in mass spectrometer miniaturization and mobilization has concomitant benefits for direct, on-site drug checking, such as reduced cost, simplified maintenance and ease of use by less skilled operators. While PS-MS technology continues to rapidly advance, it is our opinion that PS-MS can be utilized as an effective tool for harm reduction in the opioid overdose crisis.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 0.004 |
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".