Motor and cognitive decline of healthy elderly and elderly with parkinson’s disease - a cross-sectional study
Bibliographic record
Abstract
Background: Parkinson’s disease (PD) was initially described as a movement disorder, however there is now recognition that its clinical features also include non-motor symptoms such as cognitive impairment and dementia, which are frequent even in the early stages of the disease and, especially in the advanced stages. Cognitive deficits in PD include impairments in executive functions, attention, memory, and visuospatial skills. Cognitive impairment may manifest as mild cognitive impairment (MCI) or dementia, in which MCI refers to the stage between normal cognitive functioning and dementia. Factors associated with cognitive dysfunction in PD include advanced age, low schooling, worse motor scores, stiffness, postural instability and increased daytime sleepiness. Objective: To track cognitive decline and to correlate measurement instruments in subjects with PD by comparing them to healthy subjects. Methods: Study conducted at the Faculty of Health Sciences of Trairi / UFRN. The sample consisted of 20 old people (10 healthy elderlies and 10 elderlies with PD). It was applied the socio-demographic record, Unified Parkinson’s Disease Rating Scale (UPDRS II and III), Hoehn & Yahr Scale, Mini Mental State Examination, Leganés Cognitive Test (LCT) and Montreal Cognitive Assessment (MoCA). Results: It was observed cognitive decline in both groups by MoCA (90% of the PD group and 80% of the healthy group), with no statistically significant difference (p=0.10). It was also verified association between UPDRS II and LCT (r= -0.69, p=0.03) and between UPDRS III and LCT (r=-0.66, p=0.04). Conclusion: We found a cognitive deficit in the elderly group with PD, with no significant difference when compared to the healthy elderly. There was an association between motor and cognitive function in subjects with PD. MoCA was more sensitive in the screening of cognitive deficit in subjects with PD.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".