Impact of Spasticity on Balance Control during Quiet Standing in Persons after Stroke
Bibliographic record
Abstract
Background. Balance impairments, falls, and spasticity are common after stroke, but the effect of spasticity on balance control after stroke is not well understood.Methods. In this cross-sectional study, twenty-seven participants with stroke were divided into two groups, based on ankle plantar flexor spasticity level. Fifteen individuals with high spasticity (Modified Ashworth Scale (MAS) score of ≥2) and 12 individuals with low spasticity (MAS score <2) completed quiet standing trials with eyes open and closed conditions. Balance control measures included centre of pressure (COP) root mean square (RMS), COP velocity, and COP mean power frequency (MPF) in anterior-posterior and mediolateral (ML) directions. Trunk sway was estimated using a wearable inertial measurement unit to measure trunk angle, trunk velocity, and trunk velocity frequency amplitude in pitch and roll directions.Results. The high spasticity group demonstrated greater ML COP velocity, trunk roll velocity, trunk roll velocity frequency amplitude at 3.7 Hz, and trunk roll velocity frequency amplitude at 4.9 Hz, particularly in the eyes closed condition (spasticitybyvisioninteraction). ML COP MPF was greater in the high spasticity group.Conclusion. Individuals with high spasticity after stroke demonstrated greater impairment of balance control in the frontal plane, which was exacerbated when vision was removed.
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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 | 0.001 | 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".