The Athlete Biological Passport: How to Personalize Anti-Doping Testing across an Athlete's Career?
Bibliographic record
Abstract
For decades, drug testing has been the main instrument at the disposal of anti-doping authorities. The availability in the 1980s of substances identical to those produced by the human body, including the "big 3" (erythropoietin, testosterone, and growth hormone), necessitated a new paradigm in anti-doping. The athlete biological passport (ABP) is a new paradigm, complementary to traditional drug testing, based on the personalized monitoring of doping biomarkers. Athletes who abuse doping substances do so to trigger physiological changes that provide performance enhancement. The ABP aims to detect these changes through its 3 hematological, steroidal, and endocrine modules. Any deviation of a biomarker from what is expected in a healthy physiological condition can be attributable to doping or a medical condition, which, interestingly, is also the criterion used to define a banned substance. Recent advances in proteomics and metabolomics offer immense opportunities to enhance the ABP. The ABP shares multiple aspects with the present customization of health care and personalized medicine.
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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 | 0.021 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.003 | 0.005 |
| Scholarly communication | 0.007 | 0.010 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.006 | 0.008 |
| Insufficient payload (model declined to judge) | 0.013 | 0.009 |
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".