New sports, COVID-19 and the heat: sports injuries and illnesses in the Tokyo 2020 Summer Olympics
Bibliographic record
Abstract
OBJECTIVE: To describe the incidence of injuries and illnesses sustained during the Tokyo Summer Olympic Games from 23 July to 8 August 2021. METHODS: We recorded the daily number of athlete injuries and illnesses (1) through the reporting of all National Olympic Committee (NOC) medical teams and (2) in the polyclinic and medical venues by the Tokyo 2020 medical staff. RESULTS: In total, 11 315 athletes (5423 women, 48%; 5892 men, 52%) from 206 NOCs were followed up prospectively for the occurrence of injury and illness. NOC and Tokyo 2020 medical staff reported 1035 injuries and 438 illnesses, equalling 9.1 injuries and 3.9 illnesses per 100 athletes over the 17-day period. Altogether, 9% of the athletes incurred at least one injury and 4% at least one illness. The incidence of injury was highest in boxing (27%), BMX racing (27%), BMX freestyle (22%), skateboarding (21%), karate (19%) and handball (18%), of which both BMX freestyle and skateboarding were new events, and lowest in diving, road cycling, rowing, marathon swimming and shooting (1-2%). Marathon and artistic swimming presented the highest illness incidences (both 8%), followed by skateboarding and karate (both 7%). In the study period, COVID-19 affected 18 athletes, accounting for 4% of all illnesses and 0.16% of all athletes. Exertional heat illness affected 78 athletes (18% of all illnesses, 0.7% of all athletes), the majority (88%) resulting in no time lost from sport. CONCLUSION: Overall, 9% of the athletes incurred an injury and 4% an illness during the Games. Comprehensive countermeasures helped mitigate both COVID-19 and exertional heat illnesses.
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How this classification was reachedexpand
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 | 0.005 | 0.000 |
| 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) | 0.003 | 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 teacher head, 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".