Prevalence of therapeutic use exemptions at the Olympic Games and association with medals: an analysis of data from 2010 to 2018
Bibliographic record
Abstract
Objectives The percentage of athletes with Therapeutic Use Exemptions (TUEs) competing in elite sport and the association with winning medals has been a matter of speculation in the absence of validated competitor numbers. We used International Olympic Committee (IOC) and World Anti-Doping Agency (WADA) data to identify athletes competing with TUEs at five Olympic Games (Games) and a possible association between having a TUE and winning an Olympic medal. Methods We used the IOC’s competition results and WADA’s TUE database to identify the number of TUEs for athlete competitions (ACs, defined as one athlete competing in one event) and any associations with medals among athletes competing in individual competitions. We calculated risk ratios (RR) for the probability of winning a medal among athletes with a TUE compared with that of athletes without a TUE. We also reported adjusted RR (RRadj) controlling for country resources, which is a potential confounder. Results During the Games from 2010 to 2018, there were 20 139 ACs and 2062 medals awarded. Athletes competed with a TUE in 0.9% (181/20 139) of ACs. There were 21/2062 medals won by athletes with a TUE. The RR for winning a medal with a TUE was 1.13 (95% CI: 0.73 to 1.65; p=0.54), and the RRadjwas 1.07 (95% CI: 0.69 to 1.56; p=0.73). Conclusion The number of athletes competing with valid TUEs at Games is <1%. Our results suggested that there is no meaningful association between being granted a TUE and the likelihood of winning a medal.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".