Validated Ultra-Performance Liquid Chromatography-Tandem Mass Spectrometry Method for Analyzing LSD, iso-LSD, nor-LSD, and O-H-LSD in Blood and Urine
Bibliographic record
Abstract
The Royal Canadian Mounted Police Forensic Science and Identification Services was looking for a confirmatory method for lysergic acid diethylamide (LSD). As a result, an ultra-performance liquid chromatography-tandem mass spectrometry method was validated for the confirmation and quantitation of LSD, iso-LSD, N-demethyl-LSD (nor-LSD), and 2-oxo-3-hydroxy-LSD (O-H-LSD). Relative retention time and ion ratios were used as identification parameters. Limits of detection (LOD) in blood were 5 pg/mL for LSD and iso-LSD and 10 pg/mL for nor-LSD and O-H-LSD. In urine, the LOD was 10 pg/mL for all analytes. Limits of quantitation (LOQ) in blood and urine were 20 pg/mL for LSD and iso-LSD and 50 pg/mL for nor-LSD and O-H-LSD. The method was linear, accurate, and precise from 10 to 2000 pg/mL in blood and 20 to 2000 pg/mL in urine for LSD and iso-LSD and from 20 to 2000 pg/mL in blood and 50 to 2000 pg/mL in urine for nor-LSD and O-H-LSD with a coefficient of determination (R(2)) > or = 0.99. The method was applied to blinded biological control samples and biological samples taken from a suspected LSD user. This is the first reported detection of O-H-LSD in blood from a suspected LSD user.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.002 |
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".