Sports injuries and illnesses in the Sochi 2014 Olympic Winter Games
Bibliographic record
Abstract
BACKGROUND: Systematic surveillance of injuries and illnesses is the foundation for developing preventive measures in sport. AIM: To analyse the injuries and illnesses that occurred during the XXII Olympic Winter Games, held in Sochi in 2014. METHODS: We recorded the daily occurrence (or non-occurrence) of 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 Sochi 2014 medical staff. RESULTS: NOC and Sochi 2014 medical staff reported 391 injuries and 249 illnesses among 2780 athletes from 88 NOCs, equalling incidences of 14 injuries and 8.9 illnesses per 100 athletes over an 18-day period of time. Altogether, 12% and 8% of the athletes incurred at least one injury or illness, respectively. The percentage of athletes injured was highest in aerial skiing, snowboard slopestyle, snowboard cross, slopestyle skiing, halfpipe skiing, moguls skiing, alpine skiing, and snowboard halfpipe. Thirty-nine per cent of the injuries were expected to prevent the athlete from participating in competition or training. Women suffered 50% more illnesses than men. The rate of illness was highest in skeleton, short track, curling, cross-country skiing, figure skating, bobsleigh and aerial skiing. A total of 159 illnesses (64%) affected the respiratory system, and the most common cause of illness was infection (n=145, 58%). CONCLUSIONS: Overall, 12% of the athletes incurred at least one injury during the games, and 8% an illness, which is similar to prior Olympic Games. The incidence of injuries and illnesses varied substantially between 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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".