Do athletes have a right to access data in their Athlete Biological Passport?
Bibliographic record
Abstract
The Athlete Biological Passport (ABP) refers to the collection of data related to an individual athlete. The ABP contains the Haematological Module and the Steroidal Module, which are used for the longitudinal monitoring of variables in blood and urine, respectively. Based on changes in these variables, a statistical model detects outliers which indicate doping use and guide further targeted testing of the athlete. Presently, athletes can access their data of the Haematological Module in the Anti-Doping Administration and Management System (ADAMS). However, granting athletes access to this data has been a matter of debate within the anti-doping community. This article investigates whether an athlete has a right to access the contents of their ABP profile. We approached this discussion by comparing the nature of ABP data with that of forensic and medical data and touched on important concerns with ABP data disclosure to athletes such as potentially allowing for the development of alternative doping techniques to circumvent detection; and making athletes vulnerable to pressure by the media to publicly release their data. Furthermore, given that ABP data may contain medically relevant information that can be used to diagnose disease, athletes may over-interpret its medical significance and wrongly see it as a free health check. We argue that safeguarding the integrity of the ABP system must be seen as the most essential element and thus a departure from immediate data disclosure is necessary. Two different strategies for delayed data disclosure are proposed which diminish the chances of ABP data being misused to refine doping techniques.
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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 | 0.047 | 0.148 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.006 | 0.021 |
| Scholarly communication | 0.015 | 0.015 |
| Open science | 0.003 | 0.010 |
| Research integrity | 0.014 | 0.010 |
| Insufficient payload (model declined to judge) | 0.013 | 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".