Characteristics of Dysarthria and Cognitive Functions in Patients with Parkinson’s Disease and Parkinson-plus Syndrome
Bibliographic record
Abstract
Objectives: This study was to investigate the characteristics of dysarthria and cognitive ability of patients with Parkinson's disease and Parkinson-plus syndrome to find the key components that can differentiate these diseases.Methods: Forty-one patients (11 patients with idiopathic Parkinson's disease [IPD], 10 with multiple systems atrophy with predominant cerebellar ataxia [MSA-c], 10 with multiple systems atrophy with predominant Parkinsonism [MSA-p], and 10 with progressive supranuclear palsy [PSP]) participated.After controlling the motor ability in rigidity, bradykinesia, and ataxia in the Unified Parkinson's Disease Rating Scale of 4 groups, dysarthria was assessed by performing tasks of prolonged phonation, diadochokinesis, and connected speech.In addition, cognitive function was measured by the Korean version of the Montreal Cognitive Assessment.Results: The age, education level, disease duration, and motor ability of patients were not significantly different.However, analysis of motor ability showed significant (p < .05)differences between IPD-MSA-p, and MSA-c-MSA-p group for rigidity, and between IPD-MSA-c, MSA-c-MSA-p, and MSA-p-PSP group for ataxia.There was no significant difference for bradykinesia.In addition, dysarthria evaluation showed that the hypokinetic component was more frequently observed in the IPD and MSA-p than the MSA-c group and the ataxic component was greater in the MSA-c than other groups.Moreover, cognitive ability was significantly (p < .05)more impaired in patients with PSP than the IPD & MSA-c groups.Conclusion: The characteristics of dysarthria and cognitive deficits may serve as useful factors in distinguishing IPD, MSA-c, MSA-p, and PSP.Further studies including large numbers of patients are warranted to confirm these results.
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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) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".