Sports injuries and illnesses during the Winter Olympic Games 2010
Bibliographic record
Abstract
BACKGROUND: Identification of high-risk sports, including their most common and severe injuries and illnesses, will facilitate the identification of sports and athletes at risk at an early stage. AIM: To analyse the frequencies and characteristics of injuries and illnesses during the XXI Winter Olympic Games in Vancouver 2010. METHODS: All National Olympic Committees' (NOC) head physicians were asked to report daily the occurrence (or non-occurrence) of newly sustained injuries and illnesses on a standardised reporting form. In addition, the medical centres at the Vancouver and Whistler Olympic clinics reported daily on all athletes treated for injuries and illnesses. RESULTS: Physicians covering 2567 athletes (1045 females, 1522 males) from 82 NOCs participated in the study. The reported 287 injuries and 185 illnesses resulted in an incidence of 111.8 injuries and 72.1 illnesses per 1000 registered athletes. In relation to the number of registered athletes, the risk of sustaining an injury was highest for bobsleigh, ice hockey, short track, alpine freestyle and snowboard cross (15-35% of registered athletes were affected in each sport). The injury risk was lowest for the Nordic skiing events (biathlon, cross country skiing, ski jumping, Nordic combined), luge, curling, speed skating and freestyle moguls (less than 5% of registered athletes). Head/cervical spine and knee were the most common injury locations. Injuries were evenly distributed between training (54.0%) and competition (46.0%; p=0.18), and 22.6% of the injuries resulted in an absence from training or competition. In skeleton, figure and speed skating, curling, snowboard cross and biathlon, every 10th athlete suffered from at least one illness. In 113 illnesses (62.8%), the respiratory system was affected. CONCLUSION: At least 11% of the athletes incurred an injury during the games, and 7% of the athletes an illness. The incidence of injuries and illnesses varied substantially between sports. Analyses of injury mechanisms in high-risk Olympic winter sports are essential to better direct injury-prevention strategies.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".