Prevalence of therapeutic use exemptions at the Olympic Games and Paralympic Games: an analysis of data from 2016 to 2022
Bibliographic record
Abstract
OBJECTIVES: The objectives of this study are to describe the prevalence of therapeutic use exemptions (TUEs) among athletes competing in four Olympic and four Paralympic games. The secondary objective was to present the prohibited substance and methods classes associated with TUEs. METHODS: Data from the Anti-Doping Administration and Management System were extracted for this cross-sectional observation study. Eight cohorts were created to include athletes with TUEs who competed in the Rio 2016, Pyeongchang 2018, Tokyo 2020 and Beijing 2022 Olympic and Paralympic games. Prevalence of TUEs and proportion of prohibited substance and methods classes were defined as percentages among all athletes competing at each games. RESULTS: 28 583 athletes competed in four editions of the Olympic games. Total prevalence of athletes with TUEs was 0.90% among all competitors. At the four Paralympic games, a total of 9852 athletes competed and the total TUE prevalence was 2.76%. The most frequently observed substances associated with TUEs at the Summer Olympics were glucocorticoids (0.50% in Rio) and stimulants (0.39% in Tokyo). At the Summer Paralympics, diuretics (0.79% in Rio) and stimulants (0.75% in Tokyo) were the most common. Winter games had somewhat similar trends, although TUE numbers were very low. CONCLUSIONS: The number of athletes competing with valid TUEs at the Olympic and Paralympic games was <1% and <3%, respectively. Variations in substances and methods associated with TUEs for different medical conditions were identified. Nevertheless, numbers were low, further reaffirming that TUEs are not widespread in elite sport.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| 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".