Retrospective Analysis of Synthetic Cannabinoid Metabolites in Urine of Individuals Suspected of Driving Impaired
Bibliographic record
Abstract
Identification and analysis of synthetic cannabinoids (SCs) in biological specimens remains an ongoing challenge for forensic toxicologists. Analytical method development is both resource and time consuming, and falls behind the illicit production of newer SCs. Distinguishing optimal metabolic targets and specific SC use is further complicated by metabolic pathway convergence between different SCs. Gaining further insight into the prevalence and psychopharmacologic role of these drugs in forensic cases, particularly in individuals suspected of driving impaired, is important. The prevalence of SC metabolites (SCMs) in suspects of impaired driving in Washington, DC between June 2012 and August 2013 was studied. A total of 526 urine samples were screened for 12 SCMs by liquid chromatography tandem mass spectrometry in separate duplicate analyses. Nineteen cases (3.6%) confirmed positive for the following SCMs: UR-144 N-pentanoic acid (n = 17;89%), JWH-073 butanoic acid (n = 3;16%), JWH-018 pentanoic acid (n = 3;16%), AM-2201 4-hydroxypentyl (n = 3;16%) and 5-fluoro PB22 3-carboxyindole (n = 1;5%). This study made use of existing analytical methodology to provide insight into the prevalence of synthetic cannabinoid use in DUID cases. Understanding the range and extent of use in these cases can provide valuable information to the forensic community.
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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 | 0.004 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.001 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".