Development and validation of an objective balance assessment system
Bibliographic record
Abstract
Introduction: Standing balance assessments are used to help identify sports-related concussions and guide return-to-play decisions. However, field assessments of standing balance using the gold standard Balance Error Scoring System (BESS) are less accurate and reliable than laboratory based techniques. Our goal was to develop and validate a simple, reliable and affordable objective balance assessment tool for field use. Methods: Thirty healthy subjects performed the BESS test in a controlled laboratory setting wearing seven inertial measurement units (IMUs) that measured linear accelerations and angular velocities from seven landmarks on their body. All trials were filmed and each video was scored by four expert BESS raters. IMU data and mean expert BESS scores were used to develop an algorithm to compute objective BESS (oBESS) scores solely from IMU data. Inter-rater reliability and comparisons between the raters and algorithm-generated oBESS scores were assessed using intra-class correlations (ICC3,1). Results: Expert raters were consistent in scoring (ICC3,1 = 0.91), and oBESS scores computed using data from only one IMU placed at the forehead accurately fit mean expert BESS scores (ICC3,1 = 0.92) and predicted individual BESS scores (ICC3,1 = 0.90). Conclusion: The oBESS can reliably predict total BESS scores in normal subjects. With further validation, the oBESS could improve the clinical validity of on-field balance assessments for identifying sport-related concussions.
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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 | 0.011 |
| 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".