Adding the Clues to CanKet's Presence in Toxicological Casework
Bibliographic record
Abstract
Introduction: A driving under the influence of drugs (DUID) case brought on the identification of a newly emerging ketamine analogue. Following a 12-step evaluation, a drug recognition expert collected a urine sample and a nasal swab from an individual who admitted to ketamine consumption. Methods: Systematic targeted screening was performed on liquid chromatography coupled to tandem mass spectrometry (LC-MS/MS). This method covers 144 analytes, including 57 novel psychoactive substances (NPS). Further general unknown screening was performed using a gas chromatography – mass spectrometry (GC-MS) method. Results: While both urine and the nasal swab were negative for ketamine in the LC-MS/MS analysis, an interference was noted in the 7-aminonitrazepam (urine) and eutylone (urine and nasal swab) windows (erroneous ion ratio). Both items’ GC-MS analysis turned out positive for 3- fluoro-2-oxo PCE (fluorexetamine) when compared to the Cayman library. Discussion with the Canadian drug chemistry laboratory brought to light recent seizures of 2-fluoro-2-oxo PCE (CanKet). Injection of reference materials showed that these were undistinguishable under our current analytical methods. Conclusions: This case demonstrates that forensic toxicologists must remain alert to the appearance of previously undetected NPS in casework, the possibility of false identifications when relying on libraries, and subtle clues appearing in casework, such as interferences in other methods. Using the LC-MS/MS interferences as a CanKet proxy, 7 cases of DUID were subsequently sent to GC-MS analysis and confirmed to contain 2-fluoro-2-oxo PCE.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.001 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 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".