Incidence and burden of injury at the Tokyo 2020 Paralympic Games held during the COVID-19 pandemic: a prospective cohort study of 66 045 athlete days
Bibliographic record
Abstract
OBJECTIVE: To describe the epidemiology of injuries at the Tokyo 2020 Paralympic Games, including injuries sustained in the new sports of badminton and taekwondo. METHODS: Injury data were obtained daily via the established web-based injury and illness surveillance system (WEB-IISS; 81 countries, 3836 athletes) and local organising committee medical facilities (81 countries, 567 athletes). Univariate unadjusted incidences (injuries per 1000 athlete days with 95% CIs), injury proportion (IP, %) and injury burden (days lost per 1000 athlete days) are reported. RESULTS: A total of 4403 athletes (1853 women, 2550 men) from 162 countries were monitored prospectively during the 3-day pre-competition and 12-day competition periods (66 045 athlete days). 386 injuries were reported in 352 athletes (IP=8.0%) with an incidence of 5.8 per 1000 athlete days (95% CI 5.3 to 6.5). Football 5-a-side (17.2), taekwondo (16.0), judo (11.6) and badminton (9.6) had the highest incidence. There was a higher incidence of injuries in the pre-competition period than in the competition period (7.5 vs 5.4; p=0.0053). Acute (sudden onset) injuries and injuries to the shoulder (0.7) and hand/fingers (0.6) were most common. Injury burden was 10.9 (8.6-13.8), with 35% of injuries resulting in time loss from training and competition. CONCLUSION: Compared with previous Paralympic Games, there was a reduction in injury incidence but higher injury burden at the Tokyo 2020 Paralympic Games. The new sports of taekwondo and badminton had a high injury incidence, with the highest injury burden in taekwondo, compared with other sports. These findings provide epidemiological data to inform injury prevention measures for high-risk sports.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".