Longitudinal evaluation of multiple biomarkers for the detection of testosterone gel administration in women with normal menstrual cycle
Bibliographic record
Abstract
In women, hormonal fluctuations related to the menstrual cycle may impose a great source of variability for some biomarkers of testosterone (T) administration, which can ultimately disrupt the sensitivity of their longitudinal monitoring. In this study, the sensitivity of the current urinary and haematological markers of the Athlete Biological Passport (ABP), as well as serum steroid biomarkers, was investigated for the monitoring of a 28-day T gel treatment combined with endogenous fluctuation of the menstrual cycle in 14 healthy female subjects. Additionally, the analysis of urinary target compounds was performed on a subset of samples for endogenous/exogenous origin via isotope ratio mass spectrometry (IRMS). In serum, concentrations of T and dihydrotestosterone (DHT) increased significantly during the treatment, whereas in urine matrix the most affected biomarkers were found to be the ratios of testosterone/epitestosterone (T/E) and 5α-androstane-3α,17β-diol/epitestosterone (5αAdiol/E). The detection capability of both urinary biomarkers was heavily influenced by [E], which fluctuated depending on the menstrual cycle, and resulted in low sensitivity of the urinary steroidal ABP module. On the contrary, an alternative approach by the longitudinal monitoring of serum T and DHT concentrations with the newly proposed T/androstenedione ratio showed higher sensitivity. The confirmatory IRMS results demonstrated that less than one third of the tested urine samples fulfilled the criteria for positivity. Results from this study demonstrated that the 'blood steroid profile' represents a powerful complementary approach to the 'urinary module' and underlines the importance of gathering bundle of evidence to support the scenario of an endogenous prohibited substance administration.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| 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) | 0.000 | 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".