Development and Validation of an Objective Balance Error Scoring System
Bibliographic record
Abstract
INTRODUCTION: Limited access to sophisticated technology and the unreliability of simple tools prevent accurate and reliable human standing balance assessments outside research laboratory settings. The goal of this study was to develop and validate a simple objective balance assessment tool that provides an accurate, reliable, and affordable alternative to currently available laboratory and clinical methods. METHODS: Thirty healthy subjects were filmed performing the Balance Error Scoring System (BESS) while wearing inertial measurement units (IMU) measuring linear accelerations and angular velocities from seven locations of the body: forehead, sternum, waist, right and left wrist, and right and left shin. Each video was scored by four experienced BESS raters, whose mean scores were used to develop an algorithm computing objective BESS (oBESS) scores solely from IMU data. Interrater reliability and accuracy of oBESS scores were assessed using intraclass correlations (ICC). RESULTS: Raters displayed low variability in scoring (ICC3,1 = 0.91). The oBESS was able to produce scores with accurate fit to raters (ICC3,1 = 0.92) and predicted individual BESS scores (ICC3,1 = 0.90) using data from one IMU placed at the forehead. oBESS was unable to produce accurate scores (ICC3,1 = 0.68) when using IMU data from the subset of conditions (firm surface only) used in popular concussion identification protocols. CONCLUSION: The oBESS can reliably predict total BESS scores in healthy subjects. Pending further validation, oBESS could represent a valid tool to assess balance by offering an objective and reliable alternative to the current scoring methods of the BESS.
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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 | 0.008 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".