Predictive Indicators of Kinematic Writing Ability in Patients with Parkinson’s Disease: Focusing on Cognition, Depression, and Motor Abilities
Bibliographic record
Abstract
Objectives: Parkinson’s disease (PD) can affect not only motor abilities but also cognition and depression. The purpose of this study was to investigate the predictive indicators of kinematic writing ability in patients with Parkinson’s disease.Methods: Seventy-two subjects (47 patients with PD, 25 normal adults [NA]) performed tasks; including sentence writing along visual cues using software that could measure pen pressure, letter size, and writing speed.Results: Firstly, discriminant analysis showed that the writing speeds were good discriminators for the PD group. Secondly, the PD group showed positive correlation between the Korean version of the Montreal Cognitive Assessment (MoCA-K) score and pen pressure in the ‘writing within square blanks task’. They also showed a positive correlation between the Unified Parkinson’s Disease Rating Scale (UPDRS) score and letter size in the ‘free writing task’. The PD group showed a negative correlation between UPDRS score and writing speed in the ‘writing within square blank task’. Thirdly, the MoCA-K score showed significant explanatory power for pen pressure, and the UPDRS score showed significant explanatory power for the writing speed in the PD group.Conclusion: This study is significant in that it confirms the importance of considering various factors such as cognitive ability when examining the writing characteristics of patients with PD, which have been focused only on motor skills so far.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 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 | 0.000 | 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".