Prevalence of Blood Doping in Samples Collected from Elite Track and Field Athletes
Bibliographic record
Abstract
BACKGROUND: No reliable estimate of the prevalence of doping in elite sports has been published. Since 2001, the international governing body for athletics has implemented a blood-testing program to detect altered hematological profiles in the world's top-level athletes. METHODS: A total of 7289 blood samples were collected from 2737 athletes out of and during international athletic competitions. Data were collected in parallel on each sample, including the age, sex, nationality, and birth date of the athlete; testing date; sport; venue; and instrument technology. Period prevalence of blood-doping in samples was estimated by comparing empirical cumulative distribution functions of the abnormal blood profile score computed for subpopulations with stratified reference cumulative distribution functions. RESULTS: In addition to an expected difference between endurance and nonendurance athletes, we found nationality to be the major factor of heterogeneity. Estimates of the prevalence of blood doping ranged from 1% to 48% for subpopulations of samples and a mean of 14% for the entire study population. Extreme cases of secondary polycythemia highlighted the health risks associated with blood manipulations. CONCLUSIONS: When applied at a population level, in this case the population of samples, hematological data can be used to estimate period prevalence of blood doping in elite sports. We found that the world's top-level athletes are not only heterogeneous in physiological and anthropometric factors but also in their doping behavior, with contrasting attitudes toward doping between countries. When applied at the individual level, the same biomarkers, as formalized in the Athlete Biological Passport paradigm, can be used in analysis of the observed different physiological characteristics and behavioral heterogeneities.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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; 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".