Incidence and burden of illness 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 incidence and burden of illness at the Tokyo 2020 Paralympic Games, which was organised with strict COVID-19 countermeasures. METHODS: Daily illnesses were recorded via the web-based injury and illness surveillance system (teams with their own medical staff; n=81), and local polyclinic services (teams without their own medical staff; n=81). Illness proportion, incidence and burden were reported for all illnesses and in subgroups by sex, age, competition period, sports and physiological system. RESULTS: 4403 athletes (1853 female and 2550 male) from 162 countries were monitored for the 15-day period of the Tokyo Paralympic Games (66 045 athlete days). The overall incidence of illnesses per 1000 athlete days was 4.2 (95% CI 3.8 to 4.8; 280 illnesses). The highest incidences were in wheelchair tennis (7.1), shooting (6.1) and the new sport of badminton (5.9). A higher incidence was observed in female compared with male athletes (5.1 vs 3.6; p=0.005), as well as during the precompetition versus competition period (7.0 vs 3.5; p<0.0001). Dermatological and respiratory illnesses had the highest incidence (1.1 and 0.8, respectively). Illness burden was 4.9 days per 1000 athlete days and 23% of illnesses resulted in time loss from training/competition>1 day. CONCLUSION: The incidence of illness at the Tokyo 2020 Paralympic Games was the lowest yet to be recorded in either the summer or winter Paralympic Games. Dermatological and respiratory illnesses were the most common, with the burden of respiratory illness being the highest, largely due to time loss associated with COVID-19 cases. Infection countermeasures appeared successful in reducing respiratory and overall illness, suggesting implementation in future Paralympic Games may mitigate illness risk.
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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 | 0.001 |
| 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".