Cognitive Function Tests: Application of MMSE and MoCA in Various Clinical Settings- a Brief Overview
Bibliographic record
Abstract
Introduction: Cognitive impairment can emerge as part of aging or from conditions affecting brain function, such as stroke, brain tumors, delirium, and neurodegenerative diseases. Effective cognitive assessment in clinical practice requires brief, reliable tests that evaluate specific cognitive domains. The MMSE (Mini-Mental State Examination) and MoCA (Montreal Cognitive Assessment) are among the most frequently used tools for these evaluations, each offering unique insights. Purpose of Research: This study aims to compare the effectiveness of MMSE and MoCA in diagnosing cognitive impairment and determining their suitability in various clinical settings and patient profiles. Materials and Methods: The analysis includes 61 articles from databases such as PubMed and Scopus, identified using keywords: Neuropsychological Tests, Cognitive Function Tests, MMSE and MoCA. Basic Results: The results indicate that MMSE, while effective for initial dementia screening, is less sensitive to mild cognitive impairment and influenced by education and age. MoCA offers higher sensitivity for MCI and early Alzheimer's stages, making it valuable as a complementary tool to MMSE. Conclusions: Combining MMSE and MoCA assessments can enhance diagnostic accuracy across diverse clinical contexts. Each tool’s unique strengths contribute to a more comprehensive cognitive assessment approach, optimizing diagnostic strategies for specific patient needs.
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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 | 0.005 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.014 | 0.008 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| 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".