Current limitations of the Athlete's Biological Passport use in sports
Bibliographic record
Abstract
The Athletes Biological Passport (ABP) has received both criticisms and support during this year. In a recent issue of The Lancet, Michael Wozny considered that the use of the ABP makes it more difficult to take banned substances and that it was successfully used against the Italian elite cyclist Franco Pellizotti. After that, Italy's anti-doping tribunal considered that there was not enough evidence to prove manipulation of his own blood profile in Pellizotti's case. However, the UCI appealed to the Court of Arbitration for Sport (CAS) that sanctioned Pellizotti with a suspension of 2 years. Since its implementation, some problems have emerged. From 2010 to date, a large number of reports regarding the stability of the blood variables used to determine the ABP have been published, showing mixed results. This study considers that there is a risk of misinterpreting the physiological variations of the hematological parameters determined by the anti-doping authorities in the ABP. The analytical variability due to exercise training and competitions and/or to different metabolic energy demands, hypoxia treatments, etc. could lead to an increase in false-positives when using the ABP with the dramatic consequences that they might cause in major sports events like the forthcoming London Olympic Games. Moreover, the ABP characteristics, procedures, thresholds, or individual determination of reference ranges, abnormal out-comes, strikes, "how the profile differs from what is expected in clean athletes" should be clearly stated and explained in a new public technical document to avoid misunderstandings and to promote transparency.
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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 | 0.097 | 0.174 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.002 | 0.007 |
| Scholarly communication | 0.007 | 0.006 |
| Open science | 0.009 | 0.005 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.009 | 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".