Head Trauma Exposure in Mixed Martial Arts Varies According to Sex and Weight Class
Bibliographic record
Abstract
BACKGROUND: Brain injury arising from head trauma is a major concern in mixed martial arts (MMA) because knockout (KO) and technical knockout (TKO) are frequent fight outcomes. Previous studies have shown a high incidence of matches ending due to strikes to the head but did not consider weight categories and female fights. This study aimed at analyzing match stoppages in MMA and the exposure to head trauma distinguished by sex and weight categories. HYPOTHESIS: The heavier the weight class, the greater the risk and incidence of head trauma will be, regardless of sex. STUDY DESIGN: Descriptive epidemiology study. LEVEL OF EVIDENCE: Level 3. METHODS: Publicly available data of 167 MMA events from 1903 fights between 2014 and 2017 were assessed, comprising 8 male and 2 female weight categories. RESULTS: The combined KO/TKO rates per 100 athlete-exposures in the middleweight (19.53), light heavyweight (20.8), and heavyweight (26.09) divisions were greater than previously reported for MMA. While stoppage via KO/TKO occurred in 7.9% of combats in the female strawweight division, it occurred in 52.1% of the male heavyweight fights. The male middleweight ( P = 0.001), light heavyweight ( P < 0.001), and heavyweight divisions ( P < 0.001) had an increased risk of KO/TKO due to strikes to the head by 80%, 100%, and 206%, respectively. The risk in the flyweight division decreased 62% ( P = 0.001). All categories were compared with the lightweight division. The female bantamweight category presented a 221% increased risk in matches ending due to KO/TKO compared with the strawweight division ( P = 0.012). Punches to the head were the major technique used to end a combat via KO/TKO, regardless of sex and weight class. CONCLUSION: Head injury risk and incidence varies considerably according to sex and weight category in MMA. CLINICAL RELEVANCE: The analysis of head trauma exposure in MMA athletes should be distinguished according to sex and weight category.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 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) | 0.004 | 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".