Interrater Agreement of an Observational Tool to Code Knockouts and Technical Knockouts in Mixed Martial Arts
Bibliographic record
Abstract
OBJECTIVE: Interrater agreement evaluation of a tool to document and code the situational factors and mechanisms of knockouts (KOs) and technical knockouts (TKOs) in mixed martial arts (MMA). DESIGN: Retrospective case series. SETTING: Professional MMA matches from the Ultimate Fighting Championship-2006-2012. PARTICIPANTS: Two nonmedically trained independent raters. MAIN OUTCOME MEASURES: The MMA Knockout Tool (MMA-KT) consists of 20 factors and captures and codes information on match characteristics, situational context preceding KOs and TKOs, as well as describing competitor states during these outcomes. The MMA-KT also evaluates the mechanism of action and subsequent events surrounding a KO. RESULTS: The 2 raters coded 125 unique events for a total of 250 events. The 8 factors of Part A had an average κ of 0.87 (SD = 0.10; range = 0.65-0.98); 7 were considered "substantial" agreement and 1 "moderate." Part B consists of 12 factors with an average κ of 0.84 (SD = 0.16; range = 0.59-1.0); 7 classified as "substantial" agreement, 4 "moderate," and 1 "fair." The majority of the factors in the MMA-KT demonstrated substantial interrater agreement, with an average κ of 0.86 (SD = 0.13; range = 0.59-1.0). CONCLUSIONS: The MMA-KT is a reliable tool to extract and code relevant information to investigate the situational factors and mechanism of KOs and TKOs in MMA competitions.
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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 | 0.134 | 0.302 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.007 | 0.003 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.002 | 0.006 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".