Sensor-based 9-week Serial Balance Data Show Need for Individualized Baseline Profiles: Implications on Concussion Diagnosis
Bibliographic record
Abstract
Objective: The ability to accurately identify concussions and assess recovery is essential to protect individuals from experiencing negative consequences regarding premature return-to-play. To date, there is no “gold” standard of concussion diagnosis nor method to track the recovery period. Instead, clinicians rely on symptom checklists and simple tests to inform clinical decisions. However, the timing and frequency of objective measurements to screen for impairment and monitor recovery remains underexamined. This study examines the potential of a rapid (5-min) sensor-based balance measurement on a habitual (i.e., pre/post-practice) schedule to screen for concussions. Design: A pilot study using a repeated observation design. Methods: Five varsity hockey players (3 males, 2 females) were recruited for a 9-week study. Each athlete was tested prior to and after practice using an IMU, performing a modified Balance Error Scoring System (BESS) test. Results: Sampled data used to estimate individual beta distributions indicates significant individual differences in balance behaviour across a range of metrics. Conclusions: This study supports the need for individualized baseline profiles for balance in order to achieve higher accuracy and sensitivity in concussion detection. Serial, habitual testing is recommended to enable concussion detection from objective measures with higher accuracy and sensitivity during sideline assessments.
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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 | 0.002 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".