Validation of a sampling method and liquid chromatography mass spectrometry analysis method for measurement of fentanyl and five other illicit drugs
Bibliographic record
Abstract
With the increased provision of services by health authorities and community organizations allowing supervised inhalation of illicit substances comes concerns about the potential for secondhand exposure to the substances being used, whether in the adjacent community or to workers at the sites. In order to address community concerns surrounding secondhand illicit substance exposure and better protect harm reduction workers, a validated sampling and LC-MS/MS analysis method was developed for 6 illicit drugs: fentanyl, heroin, methamphetamine, cocaine, etizolam, and bromazolam. It was found that the filter used needed to be silanized to be made more inert and avoid loss of analyte due to degradation. Using the silanized filters, recoveries were good (>90%) and the collected samples were found to be stable at room temperature for 2 wk. The sampling volume validated was up to 960 L. The sensitivity and range of the method make it appropriate for short-term (15 min), full shift (8 h), or environmental sampling.
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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 | 0.005 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".