Incidence of injury and illness at the Beijing 2022 Paralympic Winter Games held in a closed-loop environment: a prospective cohort study of 7332 athlete days
Bibliographic record
Abstract
OBJECTIVE: To describe the epidemiology of injuries and illnesses sustained during the Beijing 2022 Paralympic Winter Games, organised in a closed-loop environment to adhere with COVID-19 restrictions. METHODS: Injuries and illnesses from all teams were recorded on a daily basis by team medical staff on a web-based form and by local organising committee medical (polyclinic) facilities and venue medical support. Duplicates recorded on both systems were removed. Incidence of injuries and illnesses are reported per 1000 athlete days (95% CI). RESULTS: 564 athletes (426 male and 138 female) representing 46 countries were monitored for the 13-day period of the Beijing 2022 Paralympic Winter Games (7332 athlete days). The overall incidences were 13.0 injuries (10.6-15.8) and 6.1 illnesses (4.5-8.4) per 1000 athlete days. The incidence of injury in alpine skiing (19.9; 15.2-26.1) was significantly higher compared with Nordic skiing, ice hockey and wheelchair curling (p<0.05), while the incidence of respiratory illness was significantly higher in Nordic skiing (1.6; 0.9-2.9) compared with alpine skiing, ice hockey and snowboarding (p<0.05). CONCLUSION: The incidence of both injury and illness at the Beijing 2022 Games were the lowest yet reported in the Paralympic Winter Games. The incidence of injury was highest in alpine skiing. These findings underscore the importance of ongoing vigilance and continued injury risk mitigation strategies to safeguard the well-being of athletes in these high-risk competitions. Respiratory illnesses were most commonly reported in Nordic skiing, which included the three cases of COVID-19 recorded at the games.
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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) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".