Montreal Cognitive Assessment and Mini-Mental State Examination performance in patients with mild-to-moderate dementia with Lewy bodies, Alzheimer's disease, and normal participants in Taiwan
Bibliographic record
Abstract
BACKGROUND: The aim of this study was to examine and test the sensitivity, specificity, and threshold scores of the Montreal Cognitive Assessment (MoCA) and the Mini-Mental State Examination (MMSE) and determine those that best correspond to a clinical diagnosis of dementia with Lewy bodies (DLB). METHODS: Sixty-seven Alzheimer's disease (AD), 36 DLB, and 62 healthy participants without dementia (NC), aged 60 to 90, were enrolled. All three groups took the MoCA and MMSE tests at the same time. The Cochran-Mantel-Haenszel tests and receiver operating characteristics curve analysis were used to compare the different neuropsychological test results among the groups. RESULTS: The cut-off point of the MoCA for AD was 21/22 with a sensitivity of 95.5% and a specificity of 82.3% (area under the curve (AUC): 0.945), and the cut-off point for DLB was 22/23 with a sensitivity of 91.7% and a specificity of 80.6% (AUC: 0.932). For the MMSE, the cut-off points for AD and for DLB from NC were all 24/25, with a sensitivity of 88.1% and a specificity of 85.5% for AD (AUC: 0.92), and a sensitivity of 77.8% and a specificity of 85.5% for DLB (AUC: 0.895). After controlling sex, age, and education, AD and DLB had lower scores in all MoCA subscales than the NC group (p < 0.05), except for the orientation and naming in DLB. In addition, AD had a lower score in the MoCA orientation (p = 0.03) and short-term memory (p = 0.02) than did DLB. CONCLUSIONS: The MoCA is a more sensitive instrument than the MMSE to screen AD or DLB patients from non-dementia cases.
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 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".