Injuries in martial arts: a comparison of five styles
Bibliographic record
Abstract
OBJECTIVE: To compare five martial arts with respect to injury outcomes. METHODS: A one year retrospective cohort was studied using an injury survey. Data on 263 martial arts participants (Shotokan karate, n = 114; aikido, n = 47; tae kwon do, n = 49; kung fu, n = 39; tai chi, n = 14) were analysed. Predictor variables included age, sex, training frequency ( 3 h/week), experience (<3 years v >or=3 years), and martial art style. Outcome measures were injuries requiring time off from training, major injuries (>or=7 days off), multiple injuries (>or=3), body region, and type of injury. Logistic regression was used to determine odds ratios (OR) and confidence intervals (CI). Fisher's exact test was used for comparisons between styles, with a Bonferroni correction for multiple comparisons. RESULTS: The rate of injuries, expressed as percentage of participants sustaining an injury that required time off training a year, varied according to style: 59% tae kwon do, 51% aikido, 38% kung fu, 30% karate, and 14% tai chi. There was a threefold increased risk of injury and multiple injury in tae kwon do than karate (p<0.001). Subjects >or=18 years of age were at greater risk of injury than younger ones (p<0.05; OR 3.95; CI 1.48 to 9.52). Martial artists with at least three years experience were twice as likely to sustain injury than less experienced students (p<0.005; OR 2.46; CI 1.51 to 4.02). Training >3 h/week was also a significant predictor of injury (p<0.05; OR 1.85; CI 1.13 to 3.05). Compared with karate, the risks of head/neck injury, upper extremity injury, and soft tissue injury were all higher in aikido (p<0.005), and the risks of head/neck, groin, and upper and lower extremity injuries were higher in tae kwon do (p<0.001). No sex differences were found for any of the outcomes studied. CONCLUSIONS: There is a higher rate of injury in tae kwon do than Shotokan karate. Different martial arts have significantly different types and distribution of injuries. Martial arts appear to be safe for young athletes, particularly those at beginner or intermediate levels.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".