Preserved flexibility of dynamic postural control in individuals with Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Continuous oscillation of the support base requires anticipatory and reactive postural adjustments to maintain a stable balance. In this context, postural control flexibility or the ability to adjust balance mechanisms following the requirements of the environment is needed to counterbalance the predictable, continuous perturbation of body balance. Considering the inflexibility of postural responses in individuals with Parkinson's disease (PD), maintaining stability in the support base's continuous oscillations may be challenging. Varying the frequency of platform oscillation is an exciting approach to assess the interactions between reactive and anticipatory adjustments. RESEARCH QUESTION: This study aimed to analyze postural responses of individuals with PD on an oscillatory support base across different frequencies. METHODS: Thirty participants with moderate PD diagnosis (M = 64.47 years, SD = 8.59; Hoehn and Yahr scale 3) and fifteen healthy age-matched controls (M = 65.8 years, SD = 4.2) were tested. Subjects maintained a dynamic balance on a platform oscillating in sinusoidal translations. Four oscillation frequencies were evaluated in different trials that ranged from 0.2 to 0.8 Hz in steps of 0.2 Hz. RESULTS: Analysis showed similar performance between PD and healthy participants, with modulation of amplitudes of head displacement, center of pressure, center of mass and feet-head coordination to platform oscillation frequency. DISCUSSION: Our findings suggest a preserved ability of individuals with PD to dynamically control body balance on a support base with predictable oscillatory translations.
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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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".