Preventive doping control screening analysis of prohibited substances in human urine using rapid‐resolution liquid chromatography/high‐resolution time‐of‐flight mass spectrometry
Bibliographic record
Abstract
Unification of the screening protocols for a wide range of doping agents has become an important issue for doping control laboratories. This study presents the development and validation of a generic liquid chromatography/time-of-flight mass spectrometry (LC/TOFMS) screening method of 241 small molecule analytes from various categories of prohibited substances (stimulants, narcotics, diuretics, beta(2)-agonists, beta-blockers, hormone antagonists and modulators, glucocorticosteroids and anabolic agents). It is based on a single-step liquid-liquid extraction of hydrolyzed urine and the use of a rapid-resolution liquid chromatography/high-resolution time-of-flight mass spectrometric system acquiring continuous full scan data. Electrospray ionization in the positive mode was used. Validation parameters consisted of identification capability, limit of detection, specificity, ion suppression, extraction recovery, repeatability and mass accuracy. Detection criteria were established on the basis of retention time reproducibility and mass accuracy. The suitability of the methodology for doping control was demonstrated with positive urine samples. The preventive role of the method was proved by the case where full scan acquisition with accurate mass measurement allowed the retrospective reprocessing of acquired data from past doping control samples for the detection of a designer drug, the stimulant 4-methyl-2-hexanamine, which resulted in re-reporting a number of stored samples as positives for this particular substance, when, initially, they had been reported as negatives.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 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".