Normative data for the balance error scoring system: Implications for brain injury evaluations
Bibliographic record
Abstract
PRIMARY OBJECTIVE: Patients who sustain traumatic brain injuries can experience temporary or permanent deficits in static or dynamic balance. The Balance Error Scoring System (BESS) is a brief, easily-administered test of static balance that recently has been recommended for use with military personnel who do not recover rapidly from a mild traumatic brain injury. However, the test lacks normative reference values for healthy adults, which greatly limits its clinical usefulness. The purpose of this study is to provide normative data for healthy men and women across the lifespan. METHODS: Community-dwelling adults (n = 589) between the ages of 20-69 (M = 49.75, SD = 10.81) were administered the BESS. They did not have significant medical, neurological or lower extremity problems that might have an adverse effect on balance. RESULTS: There was no relation between BESS and height and a very small correlation between BESS and weight. There was a small correlation between BESS and waist girth and body mass index. BESS performance was similar in men and women. BESS scores were consistent across the age groups until the 50s, when they worsened. Normative reference values stratified by age groups are presented. CONCLUSIONS: These normative data provide a frame of reference for interpreting BESS performance in civilians and military personnel who sustain traumatic brain injuries and adults with diverse neurological problems.
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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 | 0.023 | 0.126 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.004 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".