Rapid and quantitative determination of fentanyls and pharmaceuticals from powdered drug samples by paper spray mass spectrometry
Bibliographic record
Abstract
Paper spray mass spectrometry is presented as a direct, quantitative tool for the measurement of pharmaceutical drugs and a variety of fentanyl analogs in solid samples and powder slurries with the ultimate goal of providing meaningful harm-reduction drug checking. Method development and validation was carried out for fentanyl analog slurries as a proxy for street drug samples. Lower limits of quantitation were determined to be 3.6-7.4 ng/g for fentanyl analogs in the pharmaceutical slurry matrix. Using 1mg of solid sample, the method can quantify picogram quantities of these drugs, well below required thresholds for even the most potent fentanyl analogs. Quality control samples were prepared and used to assess method validity according to the Scientific Working Group for Forensic Toxicology (SWGTOX) guidelines. Performance metrics for both precision and accuracy were found to be within SWGTOX-recommended guidelines. Additionally, pharmaceutical tablets were used to demonstrate the applicability of the developed paper spray methodology for the direct qualitative and quantitative analysis of active ingredients in pharmaceutical powders deposited directly onto the paper spray substrate. A proposed workflow for rapid solid drug sample measurements is presented with potential applications for point-of-use street drug measurements and other solid sample matrices.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".