Static and Dynamic Assessments of Postural Control Post-Concussion
Bibliographic record
Abstract
Traditional static quiet stance balance assessments lack the environmental component to adequately assess the postural control system post-concussion injury. Literature has suggested that dynamic balance assessments are more functional and could provide greater insight into health of the postural control system post-concussion. PURPOSE: The purpose of this study was to determine whether static or dynamic postural sway assessments of Center of Pressure (CoP) directions could predict a clinically diagnosed concussion. METHODS: Fifteen collegiate athletes with concussions (AC), within 24-48 hours of a diagnosed concussion, and twenty age matched healthy collegiate athletes completed a quiet stance balance assessment and an experimental environmentally relevant balance assessment, the WiiFit Soccer Heading Game. Peak Center of Pressure (CoP) Velocity in the anteroposterior (AP) and mediolateral (ML) directions were calculated during quiet stance with eyes open and eyes closed and the WiiFit Soccer Heading Game. RESULTS: Logistic regression models of static balance CoP directions alone correctly predicted 77.1% of the clinical diagnosis of a concussion (p=0.014, R2=0.403), whereas dynamic balance CoP directions alone correctly predicted 71.4% of the cases (p=0.005, R2=0.351). A combined model of static and dynamic balance CoP directions correctly predicted 91.4% of the clinical diagnosis of a concussion (p<0.001, R2=0.727). No significant relationships were found between the static and dynamic balance CoP directions. CONCLUSIONS: These results suggest that static and dynamic balance assessments potentially measure different postural control tasks and combined have a greater chance of correctly predicting a clinically diagnosed concussion. Furthermore, this research supports the merit of both static and dynamic balance assessments as methods to determine balance dysfunctions post-concussion.
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 distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".