Contribution of Muscle Strength and Integration of Afferent Input to Postural Instability in Persons with Stroke
Bibliographic record
Abstract
OBJECTIVE: To determine the relationship of muscle strength to postural sway in persons with stroke under standing conditions in which vision and ankle proprioception were manipulated. METHODS: Forty persons with stroke and 40 healthy older adult controls were recruited from the community and underwent balance testing consisting of 6 conditions that manipulate vision and somatosensory information while standing. Postural sway was measured during each condition. In addition, lower extremity joint torques and cutaneous sensation from the plantar surface of the foot were assessed. RESULTS: Postural sway was increased with more challenging standing conditions (i.e., when multiple sensory systems were manipulated) to a greater extent with the group with stroke compared to controls. Muscle strength was only correlated to sway during the most challenging conditions. Furthermore, a greater number of persons with stroke fell during the balance testing compared to controls. CONCLUSIONS: Impairments in re-weighting/integrating afferent information, in addition to muscle weakness, appear to contribute to postural instability and falls in persons with stroke. These findings can be used by clinicians to design effective interventions for improving postural control following stroke.
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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 | 0.002 |
| 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 | 0.000 |
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".