Postural control during quiet standing and voluntary stepping response tasks in individuals post-stroke: a case-control study
Bibliographic record
Abstract
Postural control impairments following a stroke have an impact on mobility, reduce independence, and increase the risk of falls. Assessing these impairments during tasks representative of real-life situations, such as quiet standing (QS) and voluntary stepping response (VSR), will enhance our understanding of how the postural control system is impaired in individuals post-stroke (IPS). It will also inform the development of a more targeted and effective rehabilitation to prevent falls in IPS.Identify the postural control impairments encountered by IPS during QS and VSR.Twenty IPS and 16 healthy controls were recruited to perform QS and VSR tasks, while ground reaction forces and whole-body motion were measured. Displacement and speed variation of the COM, center of pressure (COP) displacement and spatiotemporal data were calculated and compared between groups.During QS, IPS exhibited greater maximal COP displacement in mediolateral direction, COM displacement in vertical direction and COM speed excursions compared to controls. During VSR, IPS exhibited smaller step length, braking force, posterior foot placement in relation to the pelvis and COM anteroposterior excursion compared to controls. IPS presented less static and dynamic postural stability compared to controls.Greater postural sway during QS, smaller anteroposterior COM displacement before losing balance and altered voluntary recovering steps during VSR could place IPS at more risk of falling when they face a postural challenge in the community. These novel results will improve the current knowledge base and should be considered in IPS rehabilitation.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".