Postural Instability and Cognitive Dysfunction in Early Parkinson's Disease
Bibliographic record
Abstract
BACKGROUND: Postural instability is one of the most disabling features of Parkinson's disease, usually occurring in late and advanced stages. The aim of this study was to investigate the postural performance of early-stage de novo Parkinson's disease patients with no clinical postural instability using computerized dynamic posturography. We sought to understand the relationship between postural sway and disease severity and the relationship between postural instability quantitatively measured by computerized dynamic posturography and cognitive impairment in early-stage Parkinson's disease patients. METHOD: Thirty-one subjects with Parkinson's disease and 20 healthy controls were assessed by the computerized dynamic posturography protocol using the sensory organization test and the motor control test. A neuropsychological assessment was also administered. RESULTS: The mean equilibrium score for sensory organization test and the vestibular input ratio were significantly correlated with Hoehn-Yahr stage. No associations between motor latency for any motor control test condition and Hoehn-Yahr stage were found. The equilibrium score for sensory organization test correlated with the mini-mental status examination scores. There was a significant correlation between motor latency for large backward translation and mini-mental status examination scores. There were significant correlations between visual perception/construction/ memory of the neuropsychological battery test and the equilibrium score for sensory organization test and between verbal word learning test, controlled word association test and motor latency for large backward translation. CONCLUSION: These findings showed the postural instability present in early-stage (Hoehn-Yahr stage 2-2.5) Parkinson's disease. We also found a close relationship between postural instability and cognitive function in Parkinson's disease patients.
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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) | 0.001 | 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".