Routine application of SFC‐MS in doping control: Analysis of 3 × 1000 urine samples using three different SFC‐MS instruments
Bibliographic record
Abstract
Supercritical fluid chromatography-mass spectrometry (SFC-MS) has proved to be a beneficial tool for sample analysis for a wide variety of compounds and, as such, has recently gained the attention of the anti-doping community. We have tested the applicability of SFC-MS for routine doping control analysing approximately 3 × 1000 identical anti-doping samples utilising SFC-MS instruments from three different vendors: Agilent Technologies, Waters Corporation and Shimadzu Corporation. A 'dilute and inject' approach either without or after hydrolysis of glucuronide metabolites was applied. Most of the compounds included in our study demonstrated excellent chromatography, whereas some showed co-elution with endogenous interferences requiring MS discrimination. Retention times typically were very stable within batches (%CV ≤ 0.5%), although this appeared to be analyte and column dependent. Chromatographic peak shape was good (symmetrical) and stable over the period of the testing without any change of column. Our results suggest that SFC-MS is a sensitive, reproducible and robust analytical tool ready to be used in anti-doping laboratories alongside the currently applied techniques such as gas and liquid chromatography coupled to mass spectrometry. Even if instruments are designed slightly differently, all three setups demonstrated their fitness for the purpose in anti-doping testing.
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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 | 0.006 | 0.009 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.003 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".