Normative Data of Mini-Mental State Examination, Montreal Cognitive Assessment, and Alzheimer’s Disease Assessment Scale-Cognitive Subscale of Community-Dwelling Older Adults in Taiwan
Bibliographic record
Abstract
INTRODUCTION: Appropriate tools and references are essential for evaluating individuals' cognitive levels. This study validated the Taiwan version of the Alzheimer's Disease Assessment Scale-Cognitive subscale (ADAS-cog) and provided normative data for the Mini-Mental State Examination (MMSE), the Montreal Cognitive Assessment (MoCA), and ADAS-cog in community-dwelling older adults. METHODS: MMSE, MoCA, and ADAS-cog were administered to 150 nondemented healthy adults aged 55-85 years during 2018-2020 as part of the Northeastern Taiwan Community Medicine Research Cohort. ADAS-cog was translated from the original English version to traditional Chinese with cultural and language considerations in Taiwan. Cronbach's alpha (α) tested the reliability of ADAS-cog, and Pearson correlations examined its external validity using MMSE and MoCA as comparisons. Normative data were generated and stratified by age and education, and the one-way analysis of variance compared scores between age and education groups. Another 20 hospital-acquired participants with cognitive impairment joined the 150 healthy participants. Comparisons in the Clinical Dementia Rating (CDR) tiers tested the discriminability of the tests for different cognitive levels. The area under the receiver operating characteristic curve (AUROC) analyzed the power of ADAS-cog in predicting CDR 0.5 from CDR 0. RESULTS: The Taiwan version of ADAS-cog had fair reliability between items (α = 0.727) and good correlations to MMSE (r = -0.673, p < 0.001) and MoCA (r = -0.746, p < 0.001). The normative data of MMSE, MoCA, and ADAS-cog showed ladder changes with age (p = 0.006, 0.001, and 0.437) and education (p < 0.001, <0.001, and <0.001) in the 150 nondemented older adults. Next, in the 170 mixed participants from the communities and the hospital, MMSE, MoCA, and ADAS-cog scores were well differentiable between CDR 0, 0.5, and 1. In addition, ADAS-cog discriminated CDR 0.5 from 0 by an AUROC of 0.827 (p < 0.001). DISCUSSION/CONCLUSION: The three structured cognitive tests consistently reflect cognitive levels of healthy older adults. The Taiwan version of ADAS-cog is compatible with MMSE and MoCA to distinguish people with mildly impaired from normal cognition. In addition, this study derived MMSE, MoCA, and ADAS-cog norms tailored to demographic factors. The findings highlight the need for stratification of age and education rather than applying a fixed cutoff for defining normal and abnormal cognition.
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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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 0.001 |
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".