Wrestling injuries during the 2016 Rio and 2020 Tokyo Olympic Games
Bibliographic record
Abstract
OBJECTIVES: To evaluate and compare the injuries of Olympic wrestlers during the 2016 Rio and 2020 Tokyo Olympic Games held in August 2021 due to the COVID-19 pandemic. METHODS: In this descriptive epidemiological study, injury report forms were used to collect and analyse injury data during the competitions. RESULTS: During 410 matches in the Rio Olympic Games, 21 injuries were recorded among 346 wrestlers (112=women), a rate of 5.1 injuries/100 bouts and 6.1 injuries/100 athletes. During 322 matches in the Tokyo Olympic Games, 28 injuries were recorded among 287 wrestlers (96=women), with 8.7 injuries/100 bouts and 9.8 injuries/100 athletes. However, these apparent differences in injury rates between Tokyo and Rio were not statistically significant (injuries/bout: p=0.057, 95% CI: 0.31 to 1.02; injuries/athlete: p=0.087, 95% CI: 0.33 to 1.08). Mild injuries comprised the greatest proportion of injuries in both Olympic Games. Severe injuries accounted for 0%, 16.7% and 36.4% of injuries in Greco-Roman, Freestyle and Women's wrestling, respectively. CONCLUSION: Most wrestling injuries in the 2016 Rio and 2020 Tokyo Olympic Games were mild skin injuries in the head and face regions due to direct body contact during standing positions in the 1/8-final round of wrestling competitions. No critical injury was observed during the recent Olympic Games. Attention should be drawn to preventing upper limb joint dislocations as common severe injuries in both Olympic Games. While not statistically significant, the Tokyo Games, after the COVID-19 pandemic, witnessed a higher injury occurrence than the Rio 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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 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) | 0.002 | 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".