The Athlete Biological Passport
Bibliographic record
Abstract
BACKGROUND: In elite sports, the growing availability of doping substances identical to those naturally produced by the human body seriously limits the ability of drug-testing regimes to ensure fairness and protection of health. CONTENT: The Athlete Biological Passport (ABP), the new paradigm in testing based on the personalized monitoring of biomarkers of doping, offers the enormous advantage of being independent of this endless pharmaceutical race. Doping triggers physiological changes that provide physiological enhancements. In the same way that disease-related biomarkers are invaluable tools that assist physicians in the diagnosis of pathology, specifically selected biomarkers can be used to detect doping. SUMMARY: The ABP is a new testing paradigm with immense potential value in the current climate of rapid advancement in biomarker discovery. In addition to its original aim of providing proof of a doping offense, the ABP can also serve as a platform for a Rule of Sport, with the presentation before competition of the ABP to objectively demonstrate that the athlete will participate in a healthy physiological condition that is unaltered by performance-enhancing drugs. Finally, the decision-support system used today for the biological monitoring of world top-level athletes can also be advantageously transferred to other areas of clinical practice to reach the goal of 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.004 |
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".