Inter‐individual variation of the urinary steroid profiles in Swedish and Norwegian athletes
Bibliographic record
Abstract
The steroidal module of the Athlete Biological Passport (ABP) aims to detect doping with endogenous steroids, e.g. testosterone (T), by longitudinally monitoring several biomarkers. These biomarkers are ratios combined into urinary concentrations of testosterone and metabolically related steroids. However, it is evident after 5 years of monitoring steroid passports that there are large variations in the steroid ratios complicating its interpretation. In this study, we used over 11000 urinary steroid profiles from Swedish and Norwegian athletes to determine both the inter- and intra-individual variations of all steroids and ratios in the steroidal passport. Furthermore, we investigated if the inter-individual variations could be associated with factors such as gender, type of sport, age, time of day, time of year, and if the urine was collected in or out of competition. We show that there are factors reported in today's doping tests that significantly affect the steroid profiles. The factors with the largest influence on the steroid profile were the type of sport classification that the athlete belonged to as well as whether the urine was collected in or out of competition. There were also significant differences based on what time of day and time of year the urine sample was collected. Whether these significant changes are relevant when longitudinally monitoring athletes in the steroidal module of the ABP should be evaluated further.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 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".