Sensitivity of doping biomarkers after administration of a single dose testosterone gel
Bibliographic record
Abstract
Micro-doping with testosterone (T) is challenging to detect with the current doping tests. Today, the methods available to detect T are longitudinally monitoring of urine biomarkers in the Athlete Biological Passport (ABP) and measuring the isotopic composition of excreted biomarkers to distinguish the origin of the molecule. In this study, we investigated the detectability of a single dose of 100 mg T gel in 8 healthy male subjects. We also studied which biomarkers were most sensitive to T gel administration, including blood biomarkers. The ABP successfully detected T gel administration in all 8 subjects. The most sensitive ratio was 5αAdiol/E, however, all ratios showed atypical findings. Isotope ratio mass spectrometry (IRMS) was performed on 5 subjects and only 2 met all the criteria for a positive test according to the rules set by the World Anti-Doping Agency (WADA). The other 3 showed inconclusive results. Other markers that were affected by T gel administration, not used for this detection today, were serum dihydrotestosterone (DHT) and T as well as reticulocyte count and percentage in whole blood. miRNA-122 was not significantly affected by the single T dose. A single dose of 100 mg T gel is possible to detect with today's doping tests. Since a single dose of T gel has an impact on some hematological biomarkers, access to both modules of the ABP when evaluating the athletes' profiles will increase the possibility to detect micro-doses of T. In addition, serum DHT and T may be a useful addition to the future endocrine module of the ABP.
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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 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".