Concussion vs. resignation by submission: Technical–tactical behavior analysis considering injury in mixed martial arts
Bibliographic record
Abstract
This research study verified the technical–tactical actions during the mixed martial arts (MMA) fights to generate serious enough injury to stop the MMA round, determining technical-tactical potential risk factors for injury in official MMA combats, according to Concussion or Resignation per Submission. A total of 990 rounds with concussions and 627 resignations per submission were considered with severe injury (i.e., a fight ended by a doctor or referee). All injuries were diagnosed and managed by attending ringside physicians during the MMA fights and rounds and had a continuous technical–tactical behavior analysis, p ≤ 0.05. The leading cause of concussion was due to head trauma (~90%), with higher dependence on head strikes scored actions. Comparisons between Concussion and Resignation per Submission combats demonstrated differences between distance head strikes actions [13(6,25) vs. 9(4,18) frequencies], clinch head strikes actions [1(0;4) vs. 1(0;3) frequencies], ground head strikes actions [1(0;8) vs. 2(0;10) frequencies] and takedowns actions [0(0;1) vs. 1(0;2) frequencies]. This information may provide significant evidence regarding the doctor stoppage in concussion combats and when it could be called by officials supervising MMA.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".