Modified Balance Error Scoring System (M-BESS) test scores in athletes wearing protective equipment and cleats
Bibliographic record
Abstract
BACKGROUND: Balance testing is an important part of the initial concussion assessment. There is no research on the differences in Modified Balance Error Scoring System (M-BESS) scores when tested in real world as compared to control conditions. OBJECTIVE: To assess the difference in M-BESS scores in athletes wearing their protective equipment and cleats on different surfaces as compared to control conditions. METHODS: This cross-sectional study examined university North American football and soccer athletes. Three observers independently rated athletes performing the M-BESS test in three different conditions: (1) wearing shorts and T-shirt in bare feet on firm surface (control); (2) wearing athletic equipment with cleats on FieldTurf; and (3) wearing athletic equipment with cleats on firm surface. Mean M-BESS scores were compared between conditions. RESULTS: 60 participants were recruited: 39 from football (all males) and 21 from soccer (11 males and 10 females). Average age was 21.1 years (SD=1.8). Mean M-BESS scores were significantly lower (p<0.001) for cleats on FieldTurf (mean=26.3; SD=2.0) and for cleats on firm surface (mean=26.6; SD=2.1) as compared to the control condition (mean=28.4; SD=1.5). Females had lower scores than males for cleats on FieldTurf condition (24.9 (SD=1.9) vs 27.3 (SD=1.6), p=0.005). Players who had taping or bracing on their ankles/feet had lower scores when tested with cleats on firm surface condition (24.6 (SD=1.7) vs 26.9 (SD=2.0), p=0.002). CONCLUSIONS: Total M-BESS scores for athletes wearing protective equipment and cleats standing on FieldTurf or a firm surface are around two points lower than M-BESS scores performed on the same athletes under control conditions.
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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 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".