Brain trauma characteristics for lightweight and heavyweight fighters in professional mixed martial arts
Bibliographic record
Abstract
Mixed martial arts (MMA) is a sport where the fighters are at high risk of brain trauma, with characteristics, such as the frequency, magnitude, and interval of head impacts influencing the risk of developing short- and long-term negative brain health outcomes. These characteristics may be influenced by weight class as they may have unique fighting styles. The purpose of this research was to compare frequency, magnitude, and interval of head impacts between lightweight and heavyweight fighters in professional MMA. Frequency, interval, event type, velocity, and location of head impacts were documented for 60 fighters from 15 Lightweight and 15 Heavyweight professional MMA fights. Head impact reconstructions of these events were performed using physical and finite element modelling methods to determine the strain in the brain tissues. The results found that LW and HW fighters sustained similar head impact frequencies and intervals. The LW fighters sustained a significantly higher frequency of very low and high magnitude impacts to the head from punches; HW a larger frequency of high category strains from elbow strikes. These brain trauma profiles reflect different fight strategies and may inform methods to manage and mitigate the long-term effects of repetitive impacts to the head.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".