Portable gas chromatography–mass spectrometry in drug checking: Detection of carfentanil and etizolam in expected opioid samples
Bibliographic record
Abstract
BACKGROUND: There has been a recent increase in adulteration of opioids with low concentration actives such as fentanyl analogues and benzodiazepines. As drug checking projects using vibrational spectroscopy continue to seek confirmatory lab-based testing, the concern and reality of missing these potentially harmful substances in point-of-care testing is prevalent. METHODS: A portable GC-MS was used to analyze select opioid samples acquired at a drug checking service in Victoria, Canada (n=59). Certified reference standards of several fentanyl analogues and benzodiazepines were measured to guide targeted analysis of these samples. Results were compared with those obtained using a lab-based paper spray mass spectrometer. RESULTS: Portable GC-MS was able to identify 62% of samples containing carfentanil and 36% of samples containing etizolam. In the case of etizolam, the success rate was higher for more potent samples: 78% of etizolam-containing samples were identified when the etizolam concentration was above 3% by weight. In comparison, infrared spectroscopy was able to detect etizolam in only 9% of the etizolam-containing samples, and is not sensitive enough to detect carfentanil at relevant concentrations. CONCLUSIONS: Portable GC-MS has potential in identifying low concentration substances in a point-of-care setting, without relying on subsequent off-site confirmatory testing.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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 | 0.001 |
| 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".