Speech, Kinematics and Gait Analysis of Degenerative Cerebellar Ataxia Syndromes
Bibliographic record
Abstract
INTRODUCTION: Cerebellar ataxias are a heterogeneous group of disorders clinically manifest as impaired coordination during a voluntary motor task resulting from cortico-cerebellar brain network dysfunction, resulting in multiple motor systems including speech, upper limb dexterous movement and gait. Objective assessment of these dysfunctional motor domains provides vital clues in assessing the underlying pathophysiology. METHODS: In this study, speech, upper limb kinematics and gait were studied using acoustic software (Praat), upper limb robot (KINARM) and gait carpet (Zeno Walkway with PKMAS). Clinical assessment was conducted using the Scale for the Assessment and Rating of Ataxia (SARA). RESULTS: In speech analysis, ataxia patients had slower and variable 'tuh' syllable repetition performance than healthy controls. In KINARM reaching tasks, ataxia patients displayed less accuracy and efficacy as measured by endpoint error (EE) and mean perpendicular error, along with slower mean and peak velocity of arm movements, prolonged reaction time and increased inter-trial variability. Moreover, there were more EEs while applying load during arm movement. Gait analysis revealed reduced cadence, reduced stride velocity, reduced step length, longer time in the double support phase and increased variability of step length, stride velocity, double support percentage and gait cycle time. CONCLUSION: The study highlights the critical role played by the cerebellum during movement execution and has paved the way for more comprehensive future studies on degenerative cerebellar ataxia, incorporating kinematic measurements in multiple motor domains.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 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".