Current limitations of the Athlete's Biological Passport use in sports
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.002 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it