Global assessment, cognitive profile, and characteristics of mild cognitive impairment in Parkinson's disease
Bibliographic record
Abstract
BACKGROUND: Cognitive deficits are evident throughout the course of Parkinson's disease (PD), with 24% of patients experiencing subtle cognitive disturbances at the time of diagnosis, and with up to 80% of patients developing PD dementia (PDD) at advanced stages of the disease PD patients with mild cognitive impairment (MCI), an at-risk phenotype of PDD, present with heterogeneous clinical characteristics that complicate the management of PD. OBJECTIVES: This study aims to examine the characteristics of PD-MCI by using the Movement Disorder Society (MDS) diagnostic criteria and evaluate the validity of global cognitive scales in identifying PD-MCI. METHODS: Seventy-nine (79) PD patients completed neuropsychological assessments and a comprehensive cognitive battery. PD-MCI was classified according to the level 2 MDS task force criteria. Mini-Mental State Examination (sMMSE), Montreal Cognitive Assessment (MoCA) and Parkinson's Disease Cognitive Rating Scale (PDCRS) were examined against a level 2 dichotomised PD-MCI diagnosis. Characteristics of PD-MCI were evaluated using logistic regression analysis. RESULTS: Twenty-seven patients met criteria for PD-MCI (34%). The MoCA and PDCRS demonstrated high validity to screen for PD-MCI. Impairments in multiple cognitive domains were observed in 77.8% of PD-MCI patients. There were significantly more males in the PD-MCI group compared to PD patients without MCI (p < 0.01). CONCLUSIONS: PD patients with MCI exhibited impairments in the attention/working memory, executive function and memory domains. Heterogeneous cognitive characteristics in PD warrant further investigation into specific cognitive subtypes to advance understanding and effective evaluation of PD-MCI.
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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 | 0.001 |
| 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".