Pause characteristics of sentence production in Parkinson's disease: insights from sentence complexity and length
Bibliographic record
Abstract
PURPOSE: Parkinson's disease (PD) affects forward flow of speech including fluency disruptions in 90% of individuals. One of the main parameters affecting flow and fluency of speech is pause behaviour. However, the precise language characteristics of pauses, including sentence complexity and length, and how they contribute to the fluency disruptions of PD are not fully understood. This study examined how sentence complexity and length affect pause behaviour in PD. METHOD: Seventy-one participants, comprising individuals with PD (n = 32) and neurotypical controls (n = 39), read a speech passage aloud. The number and duration of pauses, categorised by location (between, within sentences), sentence complexity (simple, complex), and sentence length (short, long) were analysed. Cognitive ability, assessed using the Montreal Cognitive Assessment (MoCA), and motor speech deficits (i.e., dysarthria) severity, assessed using a speech perceptual ranking, were evaluated and correlated with pause characteristics. RESULTS: Individuals with PD produced significantly more pauses across all categories compared to controls. However, only between-sentence and long-sentence pauses were significantly longer in duration. Pause frequency and duration in both groups were higher in more complex and longer sentences. Significant negative correlations were found between MoCA scores and number of pauses. Significant positive correlations were observed between dysarthria severity and duration of pauses. CONCLUSION: These findings suggest that increased cognitive-linguistic demands-indexed by sentence complexity and length-may underlie pausing behaviour and contribute to fluency disruptions in individuals with PD. The results extend previous research by highlighting the potential cognitive-linguistic basis of motor speech dysfunction in PD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".