Application of fast gas chromatography/mass spectrometry for the rapid screening of synthetic anabolic steroids and other drugs in anti‐doping analysis
Bibliographic record
Abstract
This paper describes a fast gas chromatographic/mass spectrometric (GC/MS) screening method for the detection, in urine, of 36 xenobiotics (30 synthetic anabolic steroids, four narcotics, one diuretic and one stimulant) excreted free or as glucuro-conjugates in urine and detectable as trimethylsilyl (TMS) derivatives. These drugs (and/or their urinary metabolites) can be simultaneously extracted by a single liquid/liquid separation step, at alkaline pH, after enzymatic hydrolysis with beta-glucuronidase and then assayed as TMS derivatives by GC/MS using electron ionisation (EI) and single ion monitoring (SIM) acquisition mode. The total time needed for the GC run is less than 8 min. Good reproducibility of the retention times (CV% <1) and the relative abundances of the diagnostic fragment ions (CV% <10) was observed for all target analytes. The sensitivity of the method is sufficient to match the requirements of the World Anti-Doping Agency (WADA) for the accredited laboratories, with limits of detection (LODs) that are lower than the corresponding WADA minimum required performance limits (MRPLs) for all target compounds.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".