Traumatic brain injuries in mixed martial arts: A systematic review
Bibliographic record
Abstract
Introduction Mixed martial arts is an emerging combat sport that is gaining popularity worldwide. We systematically reviewed the literature regarding the prevalence, severity and risk factors of head injuries sustained in mixed martial arts activities. Methods We conducted a comprehensive systematic review of Ovid MEDLINE, Embase, PsycINFO, EBM Reviews, CINAHL, SPORTDiscus, and Web of Science from 1990 to 2016 for studies of any design that reported associations of acute or chronic head injuries in persons participating in mixed martial arts activities. Results The initial database search yielded a total 472 citations, including 264 unique citations after duplications were removed. A total of 18 articles, primarily of observational data, showed ‘technical knockouts’ and ‘knockouts’ are prevalent in this sport (range: 28.3–46.2% of all matches) with other studies showing the lifetime average of 6.2 technical knockouts or knockouts in a career. Studies used inconsistent reporting methods for concussion, and no information regarding long-term follow-up was available. Conclusion Mixed martial arts fighting may be associated with repetitive head injuries and potential long-term neurological consequences; however, data on this topic are poor. Larger studies and stringent medical oversight are needed to improve the management and understanding of mixed martial arts head injuries, with implementation of harm reduction strategies and/or rule modifications to prevent long-term neurological sequelae. Systematic Review Registration: PROSPERO – CRD42014010019.
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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 | 0.007 | 0.035 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.012 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".