Risk factors associated with injury and concussion in sanctioned amateur and professional mixed martial arts bouts in Calgary, Alberta
Bibliographic record
Abstract
BACKGROUND: There is limited literature that examines risk factors for injury and mild traumatic brain injury (mTBI) in mixed martial arts (MMA). An examination of previously unstudied bout and athlete characteristics that may pose health risks while partaking in this sport is warranted. HYPOTHESIS/PURPOSE: To determine the incidence of injury and concussion, along with the identification of risk factors that contribute to injury and mTBI in amateur and professional MMA bouts in Calgary, Alberta. STUDY DESIGN: A retrospective cohort study with case-control design. METHODS: Calgary amateur and professional MMA records were examined from 1 January 2010 to 31 December 2015. Descriptive statistics were used to describe the incidence of injury and concussion, along with univariate and multivariable logistic regression to identify risk factors for injury and mTBI. RESULTS: The injury rate per 100 athlete exposure (AE), the injury rate per 100 min of exposure and the concussion rate per 100 AE were 23.6 (95% CI 20.5 to 27.0), 4.1 (95% CI 3.48 to 4.70) and 14.7 (95% CI 11.8 to 17.2), respectively. The most common location of injury was the head and mTBI was the most common type of injury. Athletes whose bout was finished by a knockout/technical knockout, corner stoppage, draw, no contest or physician, and those whose country of origin was non-Canadian, were more likely to sustain an injury. No risk factors for concussion were shown to be significant. CONCLUSION: Engaging in MMA exposes athletes to inherent risk and several recommendations are proposed to reduce these risks. Future prospective investigations are necessary to better delineate the findings in this study.
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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 | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".