Combative Sports Injuries
Bibliographic record
Abstract
OBJECTIVE: Mixed martial arts (MMA) is an increasingly popular combative sport involving aggressive techniques that present substantial injury risk. We examined the incidence and types of injuries sustained in MMA fights and compared this with injuries sustained in boxing matches. DESIGN: Consecutive Case Series. SETTING: We used data from post-fight medical examinations on all bouts in Edmonton, Canada, between 2000 and 2013. PARTICIPANTS: The participants were 1181 MMA competitors and 550 boxers. MAIN OUTCOME MEASURES: The attending physician conducted a mandatory post-fight examination of all fighters and documented the nature of injuries sustained. RESULTS: Boxers were significantly more likely not to experience injury (49.8% vs 59.4%, P < 0.001), whereas MMA fighters were significantly more likely to experience 1 injury (typically contusion/bruising, P < 0.001). Boxers were more likely to experience loss of consciousness (7.1% vs 4.2%, P = 0.01) and serious eye injury (1.1% vs 0.3%, P = 0.02). CONCLUSIONS: The overall injury incidence in MMA competitors appears slightly higher than for boxers, but MMA fighters experience more minor contusion/bruising injuries. Boxers are more likely to experience serious injury such as concussion/head trauma involving loss of consciousness or eye injury such as retinal detachment.
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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 | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.017 | 0.002 |
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".