Correlation between motor and cognitive functions in the progressive course of Parkinson's disease
Bibliographic record
Abstract
Abstract Aim Correlations between motor function and frontal‐executive function in Parkinson's disease ( PD ) have been examined previously, but correlations with other cognitive domains remain unknown. We examined the correlation between motor dysfunction and cognitive impairment with regard to their precise domains. Methods Motor and cognitive functions were assessed in 61 patients. To assess motor function, the Unified Parkinson's Disease Rating Scale ( UPDRS ) was administered. The UPDRS score was assessed as general motor function with a sum of Parts II and III , and as subscores of individual motor symptoms (rigidity, tremor, akinesia and postural instability). To assess cognitive function, the Montreal Cognitive Assessment (Mo CA ) and the Frontal Assessment Battery ( FAB ) were administered. Mo CA was assessed by a total score and subscores of six cognitive subdomains: visuospatial, executive, attention/concentration/working memory, language, memory and orientation. The correlation coefficients of both Mo CA and FAB with patient background and motor symptoms were compared using Spearman's correlation coefficient. Results General motor function and the subscore of postural instability showed significant negative correlations with Mo CA , FAB , and the subdomains of visuospatial, executive and orientation skills. Tremor and rigidity showed no significant correlation with any cognitive assessment. Akinesia showed significant negative correlation with Mo CA , and the subdomains of visuospatial and orientation skills. Conclusions In patients with PD , specific motor and cognitive functions correlate, with particular regard to postural instability and visuospatial skills. The correlations suggest functional links between a number of cerebral cortices and subcortical structures, and a common pathophysiology for motor and cognitive impairments in PD .
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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 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".