Perception of Weight-Bearing Distribution During Sit-to-Stand Tasks in Hemiparetic and Healthy Individuals
Bibliographic record
Abstract
BACKGROUND AND PURPOSE: It is unknown whether hemiparetic individuals are aware of their weight-bearing asymmetry during sit-to-stand tasks. This study compared the error between hemiparetic and healthy individuals' perception of weight-bearing and their actual weight-bearing distribution during the sit-to-stand task and analyzed the association between the knee extensor muscle strength and the weight-bearing distribution and perception. METHODS: Nineteen unilateral hemiparetic subjects and 15 healthy individuals participated in the study. They performed the sit-to-stand transfer on force platforms under different foot placements (spontaneous and symmetrical) and had to rate their perceived weight-bearing distribution at the lower limbs on a visual analog scale. The strength of the knee extensors was assessed with a Biodex dynamometer. RESULTS: The hemiparetic individuals presented greater weight-bearing asymmetry and errors of perception than the healthy individuals. Although no significant association was found between strength and weight-bearing perception, moderate associations were found between strength and weight-bearing distribution for both the spontaneous (r=0.75, P<0.01) and symmetrical (r=0.71, P<0.01) foot position conditions. CONCLUSIONS: This study revealed that individuals with hemiparesis after a stroke do not perceive themselves as asymmetrical when executing the sit-to-stand transfer and that the knee extensor strength is a factor linked to their weight-bearing asymmetry, not to their perception.
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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 | 0.001 |
| 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".