Longitudinal Profiling of Endogenous Steroids in Blood Using the Athlete Biological Passport Approach
Bibliographic record
Abstract
CONTEXT: Detection of endogenous anabolic androgenic steroids (EAAS), like testosterone (T), as doping agents has been improved with the launch of the Steroidal Module of the Athlete Biological Passport (ABP) in urine samples. OBJECTIVE: To target doping practices with EAAS, particularly in individuals with low level of biomarkers excreted in urine, by including new target compounds measured in blood. DESIGN: T and T/androstenedione (T/A4) distributions were obtained from 4 years of anti-doping data and applied as priors to analyze individual profiles from 2 T administration studies in female and male subjects. SETTING: Anti-doping laboratory. Elite athletes (n = 823) and male and female clinical trials subjects (n = 19 and 14, respectively). INTERVENTION(S): Two open-label administration studies were carried out. One involved a control phase period followed by patch and then oral T administration in male volunteers and the other followed female volunteers during 3 menstrual cycles with 28 days of daily transdermal T application during the second month. MAIN OUTCOME MEASURE(S): Serum samples were analyzed for T and A4 and the performance of a longitudinal ABP-based approach was evaluated for T and T/A4. RESULTS: An ABP-based approach set at a 99% specificity flagged all female subjects during the transdermal T application period and 44% of subjects 3 days after the treatment. T showed the best sensitivity (74%) in response to transdermal T application in males. CONCLUSIONS: Inclusion of T and T/A4 as markers in the Steroidal Module can improve the performance of the ABP to identify T transdermal application, particularly in females.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".