Tests of Static Balance Do Not Predict Mobility Performance Following Traumatic Brain Injury
Bibliographic record
Abstract
PURPOSE: To investigate the extent to which different single-limb support (SLS) parameters predict mobility performance following traumatic brain injury (TBI). METHODS: Seventy-one people with mobility limitations following TBI were assessed for balance and mobility performance in a human movement laboratory. Participants performed a clinical test of static balance that involved balancing in SLS on each leg with eyes open and eyes closed. Mobility performance was measured by self-selected gait speed and performance on the High Level Mobility Scale (HiMAT). Dynamic stability during walking was measured by quantifying lateral centre of mass (COM) displacement, width of base of support, and proportion of double-support stance time. RESULTS: Total static balance scores were strongly correlated with HiMAT scores (r=0.57, p<0.001) and lateral COM displacement (r=-0.51, p<0.001). Despite these strong correlations, however, balance scores explained only 32% of the variance in advanced mobility skills (r(2)=0.32) and 26% of the variance in lateral COM displacement (r(2)=0.26). CONCLUSIONS: Since mobility performance varied widely for people with similar levels of balance, SLS time was not able to predict dynamic stability during gait, self-selected gait speed, or advanced mobility skills in people with TBI.
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 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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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 | 0.001 |
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".