The Reliability of the Modified Balance Error Scoring System
Bibliographic record
Abstract
OBJECTIVE: Study 1 investigated the intraclass reliability and percent variance associated with each component within the traditional Balance Error Scoring System (BESS) protocol. Study 2 investigated the reliability of subsequent modifications of the BESS. DESIGN: Prospective cross-sectional examination of the traditional and modified BESS protocols. SETTING: Schools participating in Georgia High School Athletics Association. INTERVENTION: The modified BESS consisted of 2 surfaces (firm and foam) and 2 stances (single-leg and tandem-leg stance) repeated for a total of three 20-second trials. PARTICIPANTS: Participants consisted of 2 independent samples of high school athletes aged 13 to 19 years. MAIN OUTCOME MEASURES: Percent variance for each condition of the BESS was obtained using GENOVA 3.1. An intraclass reliability coefficient and repeated measures analysis of variance were calculated using SPSS 13.0. RESULTS: Study 1 obtained an intraclass correlation coefficient (r = 0.60) with stance accounting for 55% of the total variance. Removing the double-leg stance increased the intraclass correlation coefficient (r = 0.71). Study 2 found a statistically significant difference between trials 1 and 2 (F(1.65,286) = 4.890, P = 0.013) and intraclass reliability coefficient of r = 0.88 for 3 trials of 4 conditions. CONCLUSIONS: The variance associated with the double-leg stance was very small, and when removed, the intraclass reliability coefficient of the BESS increased. Removal of the double-leg stance and addition of 3 trials of 4 conditions provided an easily administered, cost-effective, time-efficient tool that provides reliable objective information for clinicians to base clinical decisions upon.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.015 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".