Interrater Agreement of an Observational Tool to Code Knockouts and Technical Knockouts in Mixed Martial Arts
Why this work is in the frame
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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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Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.017 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 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 it