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.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.004 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.005 | 0.009 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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; both teacher heads agree on what is shown here.
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".