Clinical Correlates of Between-Limb Synchronization of Standing Balance Control and Falls During Inpatient Stroke Rehabilitation
Bibliographic record
Abstract
BACKGROUND: Stroke-related sensorimotor impairment potentially contributes to impaired balance. Balance measures that reveal underlying limb-specific control problems, such as a measure of the synchronization of both lower limbs to maintain standing balance, may be uniquely informative about poststroke balance control. OBJECTIVE: This study aimed to determine the relationships between clinical measures of sensorimotor control, functional balance, and fall risk and between-limb synchronization of balance control. METHODS: The authors conducted a retrospective chart review of 100 individuals with stroke admitted to inpatient rehabilitation. Force plate-based measures were obtained while standing on 2 force plates, including postural sway (root mean square of anteroposterior and mediolateral center of pressure [COP]), stance load asymmetry (percentage of body weight borne on the less-loaded limb), and between-limb synchronization (cross-correlation of the COP recordings under each foot). Clinical measures obtained were motor impairment (Chedoke-McMaster Stroke Assessment), plantar cutaneous sensation, functional balance (Berg Balance Scale), and falls experienced in rehabilitation. RESULTS: Synchronization was significantly related to motor impairment and prospective falls, even when controlling for other force plate-based measures of standing balance control (ie, postural sway and stance load symmetry). CONCLUSIONS: Between-limb COP synchronization for standing balance appears to be a uniquely important index of balance control, independent of postural sway and load symmetry during stance.
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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 | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| 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".