Investigating the usage of motion verbs in Parkinson's disease
Bibliographic record
Abstract
Background/Purpose: This study aimed to determine the quantity of motion verbs in Parkinson’s Disease (PD) and their potential correlation with other parameters. Methods: In this study, 20 participants diagnosed with Parkinson's disease (mean age 68.45±10.5; 14 males; 6 females) were included. They were recruited at the Maltepe University Hospital, Faculty of Medicine, Department of Neurology. Montreal Cognitive Assessment (MoCA), the pear film, and animated short videos were used as data collection tools. Results: Among the participants, 60% were in the early stages of PD, and 40% were in the advanced stages, with educational backgrounds ranging from primary school to university. Statistical analysis showed no significant differences in the usage of verbs, motion verbs, and participles across PD stages, gender, or education levels (p > 0.05). Conclusion: In this study, no significant difference was found in the use of motion verbs among individuals with PD. The literature suggests that motion verb impairments in PD patients are typically attributed to deficits in executive functions, and that motor cortex atrophy does not contribute to these impairments. As a result, there remains no consensus regarding the precise nature of language deficits in Parkinson’s disease.
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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 | 0.003 |
| 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 | 0.001 | 0.001 |
| 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".