Screening for unknown synthetic steroids in human urine by liquid chromatography‐tandem mass spectrometry
Bibliographic record
Abstract
Chemically modified steroids (designer steroids), including tetrahydrogestrinone and norbolethone, pose a threat to the integrity of the sport community. These compounds have recently been detected in urine specimens from athletes, resulting in temporary or permanent suspension from amateur and/or professional competition. Triple quadrupole mass spectrometers enable doping control laboratories to screen for unknown, anabolic, androgenic steroids utilizing precursor ion scans. On the basis of common dissociation patterns of steroids with common structural features, characteristic product ions were selected to serve as diagnostic markers for previously unidentified drugs or drug metabolites in human urine samples. An assay was established to complement standard screening procedures. Urine specimens were enzymically hydrolyzed, partitioned into ether, concentrated, and analyzed by precursor ion scanning. Spectra from samples fortified with eight standard compounds (methyltestosterone, ethyltestosterone, 1-testosterone, gestrinone, dihydrogestrinone, tetrahydrogestrinone, norbolethone, and propyltrenbolone) and one deuterium-labeled analog (d(4)-tetrahydrogestrinone) at 50 ng/ml of urine, had precursor ion peaks other than those from common endogenous steroids. Subsequent product ion scan experiments on precursor ions of peaks of unknown origin provided structural identification of the unknown 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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.000 |
| 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".