Application of mini-mental state examination and Montreal Cognitive Assessment in the diagnosis of dementia with Lewy bodies and Alzheimer’s disease
Bibliographic record
Abstract
BACKGROUND: Dementia with Lewy Bodies (DLB) and Alzheimer's disease (AD) are two types of dementia with a relatively high incidence, and their clinical manifestations are easily confused. However, the cognitive impairment characteristics of the two diseases are different, and the results of cognitive assessment can help the diagnosis of the disease. OBJECTIVE: To explore the different characteristics of Mini-mental State Examination (MMSE) and Montreal Cognitive Assessment Scale (MoCA) in DLB and AD patients, and to explore potential markers to distinguish AD and DLB. METHODS: This study included 66 patients with DLB, 81 with AD, and 58 cognitively normal subjects. All of them completed MMSE, MoCA, and Clinical Dementia Rating (CDR). RESULTS: > 0.05). CONCLUSION: We observed distinct cognitive performances in subjects from both the DLB and AD groups across different stages of dementia. Our study confirms the high value of MMSE and MoCA in distinguishing patients with DLB and AD in the early stages of the disease, and they can improve the differential diagnosis of DLB and AD.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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) | 0.000 | 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 teacher head, 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".