Comparing speech to fine and gross motor skills in Parkinson’s patients
Bibliographic record
Abstract
Parkinson’s Disease (PD) is a neurodegenerative motor disorder resulting from damage to dopaminergic neurons. The goal of this study is to evaluate the correspondence between speech and non-speech motor impairments. To explore this, we extract features from the mPower dataset [B. M. Bot et al., Sci Data 3, 160011 (2016)] containing mobile data from PD patients and healthy controls along with their performance on a vowel phonation, finger tapping, and walking task. We hypothesize that there is a shared motor system underlying each of these modalities and that disease progression will manifest in impairments to both speech and non-speech systems that rely on motor control. For acoustic features, we measure temporal consistency via F0-independent features (shimmer, jitter, and harmonics-to-noise ratio). For non-acoustic tasks, we adapt this set to measure spatial consistency and accuracy in finger tapping or walking. We perform clustering and multidimensional scaling (MDS) on our features to understand their correspondence across the modalities. Results will be reported with relevance to the relationship between PD and its effects on articulatory and general motor processes.
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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 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".