Evaluation of the early weigh-in policy for mixed martial arts events adopted by North American athletic commissions
Bibliographic record
Abstract
Objective To characterize the epidemiology of overweight athletes before and after the introduction of the Early Weigh-In Policy (EWIP).Methods A retrospective cohort study examined the weigh-in results for professional mixed martial arts (MMA) events over a 2-year period around the introduction of the new EWIP between 2014 and 2018. Descriptive statistics were used to characterize the study populations. Risk ratios were used to identify differences in the study populations before and after the introduction of the EWIP.Results After the introduction of the EWIP, the number of overweight athletes increased from 5.7% to 8.4% and the average overweight mass increased from 1.3 kg (2.9 lbs) to 1.8 kg (3.9 lbs) [difference, 0.5 kg (1.0 lb), p = 4.35 × 10^(−5)]. The proportion of athletes is not distributed similarly across the different overweight mass categories when comparing the pre- and post-EWIP time frames (p = 0.006). More athletes in the pre-EWIP period were overweight by smaller amount, while in the post-EWIP period athletes were overweight by larger amount. Of the athletes who were overweight before the regulation change, 28.7% were over the weight limit by greater than 1.8 kg (4 lbs), compared to 39.5% after the new EWIP introduction. On average, the ratio of overweight athletes per events by commission was 1.2 before the introduction of the EWIP and 2.1 after.Conclusion These results appear to indicate that the EWIP has not altered weight-cutting culture in MMA in a positive manner. This study casts doubt on the benefits of an EWIP and raised the possibility of utilizing the longitudinal weight monitoring approach to mitigate rapid weight-cycling behavior. However, before additional changes are made by any athletic commission, further research is needed to examine the efficacy of the abovementioned longitudinal weight monitoring approach or any other strategy.
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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 | 0.010 | 0.027 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".