Is the Recovery of Functional Balance and Mobility Accompanied by Physiological Recovery in People With Severe Impairments After Stroke?
Bibliographic record
Abstract
BACKGROUND: Rehabilitation after severe stroke is often limited because of impairments in sensorimotor function. Functional and physiological recovery after severe stroke is poorly understood and has not been studied extensively. OBJECTIVE: This study's purpose was to examine functional and physiological recovery of standing balance during inpatient rehabilitation in people with severe impairments after stroke. METHODS: A total of 10 participants with severe impairments after stroke were evaluated monthly in a stroke rehabilitation unit with the following functional outcome measures: Berg Balance Scale (BBS), Clinical Outcome Variables Scale (COVS), and Chedoke McMaster Stroke Assessment (CMSA). Weight bearing (WB), center of pressure (COP) velocity, and electromyography (EMG) data were collected during quiet standing and during internal perturbation with a rapid nonparetic arm raise. RESULTS: Cross-sectionally, there were moderate to strong correlations for EMG area and WB with CMSA and COVS. Additionally, the BBS was correlated with WB on the paretic side. Longitudinally, statistically significant improvement was found for functional measures but not for physiological measures. The mean BBS and COVS improved by 23 and 21 points, respectively. COP velocity decreased by 60.1% on the paretic leg but not significantly. CONCLUSIONS: During stroke rehabilitation, all participants improved functionally. Some patients improved physiologically, though near discharge, all participants remained very impaired. Future studies with larger sample sizes are needed to explore the capacity for physiological recovery in this population.
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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 | 0.001 | 0.004 |
| 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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".