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 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 | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| 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".