Wrestling Injuries During the 2008 Beijing Olympic Games
Bibliographic record
Abstract
BACKGROUND: Better understanding of the incidence, mechanisms, and characteristics of potential injuries in wrestling helps to implement preventive measures to better care for these athletes. Several studies have investigated the incidence and type of injuries in amateur and intercollegiate wrestling; however, there is a lack of studies that review the incidence and nature of injuries in elite wrestlers during Olympic Games or World Championships. PURPOSE: The purpose of this study was to assess the injury profile of elite senior wrestlers in Greco-Roman, freestyle, and female wrestling during the 2008 Beijing Olympic Games. STUDY DESIGN: Descriptive epidemiologic study. METHODS: Study participants consisted of 343 wrestlers participating in the 2008 Beijing Olympic Games. Standard checkoff forms were used to collect the injury data, including injury type, severity, location, timing, and mechanism. RESULTS: A total of 343 athletes sustained 32 injuries during 406 matches, which is equivalent to an overall incidence of 9.30 injuries per 100 athletes and 7.88 injuries per 100 matches. Among the 3 styles, freestyle had the highest injury rate (10.1%) and female wrestling the lowest (7.5%). In sum, 84.4% of all injuries were categorized as mild. Although the overall injury rate in male athletes was slightly higher than that among female athletes (9.7% versus 7.5%), this difference was not statistically significant (odds ratio = 1.21, 95% confidence interval = 0.46-3.68; P = .40). CONCLUSION: The rate and severity of wrestling injuries during the 2008 Beijing Olympic Games were lower than previous reports. No serious and catastrophic injury was recorded, and most injuries were minor.
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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 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 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".