Screening of cognitive impairment in early stage parkinson disease with Montreal cognitive assessment scale
Bibliographic record
Abstract
Objective To compare the ability of Montreal Cognitive Assessment (MoCA) and Mini-Mental State Examination (MMSE) in screening cognitive impairment in early stage of Parkinscn disease (PD). Methods The cognitive function of 101 patients with Parkinson disease (Hohen-Yahr stage 1-3) was assessed with MMSE. Ninety-six patients defined as having a normal age- and education-adjusted MMSE score were assessed subsequently with MoCA. The 96 patients were divided into two groups according to cut-off points of 26 of MoCA. The performance of cognitive domain was compared between PD-MCI group (MoCA <26) and control group (MoCA≥26). Results Mean MMSE and MoCA scores (standard deviation) were 27.17 (2.69) and 22.60(4.42) , respectively. 75% of the patients with normal MMSE scores had cognitive impairment according to their MoCA score. The PD-MCI group had lower scores in numerous cognitive domains (visuospatial and executive abilities, naming, attention,language, ab-straction, delayed memory) compared with control group (PD-MCI group: 3.11±1.40,2.56±0.69,5.07±1.05, 1.69±0.85,1.08±0.84, 1.08±1.31 ;Control group:4.75±0.61,2.92±0.28,5.88±0.45,2.46±0.66, 1.92±0.28,3.50±0.78, P<0.05). Predictors of cognitive impairment on the MoCA using univariate analyses were gender, age, education, Hoehn-Yabr stage, Unified Parkinscn Disease Rating Scale, depression severity (HAMD) and hallucination (r was-0.205,-0.209,0.263,-0.352,-0.225,-0.293 and-0.218, respectively). Condusions The MoCA is a more sensitive screening than the MMSE for cognitive impairment in early stage of PD. Key words: Parkinson disease; Cognitive impairment; Assessment
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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) | 0.002 | 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".