Analysis of Testosterone Esters in Serum and DBS Samples—Results From an Interlaboratory Study
Bibliographic record
Abstract
Testosterone (T) formulations that are used for doping purposes often contain the steroid in esterified forms. As these esters are hydrolysed in the bloodstream before renal excretion, they can be detected in blood matrices and have not been detected in urine so far. Serum samples can additionally be used for longitudinal blood steroid profiling, but their collection, shipping and storage have some disadvantages. The use of dried blood spots (DBS), an alternative blood matrix, is more convenient for pre-analytical and post-analytical aspects but is not fully established in antidoping laboratories yet. To evaluate the ability of multiple antidoping laboratories to detect T-esters in serum and DBS samples, an interlaboratory study was organised. Common T-esters were spiked in five samples of each matrix (serum, cellulose card DBS, polymeric DBS) at concentrations that correspond to an administration scenario and sent as blinded specimens to each laboratory. The laboratories were requested to apply their own analytical method to detect the T-esters and to provide a rough estimate of their concentrations. All laboratories identified the spiked testosterone esters correctly in all samples and the estimated concentrations were deemed comparable (average relative standard deviation < 30%), considering that only qualitative initial testing procedures (ITPs) were used. This study could firstly demonstrate the capability of different analytical approaches to analyse T-esters in serum and DBS samples and, secondly, show that the methods employed by the participating laboratories are all fit for purpose.
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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 | 0.015 | 0.016 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".