Can MoCA and MMSE Be Interchangeable Cognitive Screening Tools? A Systematic Review
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Cognitive disorders may be an early sign of neuropsychiatric disorders; however, it remains unclear whether the screening measures are interchangeable. The aim of this study was to contrast the most commonly used screening tools-Mini-Mental State Examination (MMSE) and Montreal Cognitive Assessment (MoCA)-for early detection of neurocognitive disorder (NCD). RESEARCH DESIGN AND METHODS: This study presents a descriptive systematic review and informative literature according to the Cochrane Foundation's guidelines. The keywords "Mini-Mental State Examination" and "Montreal Cognitive Assessment" were searched in the Web of Science, SciELO, and LILACS databases. RESULTS: Fifty-one studies were selected including a total sample of 11,870 participants (8,360 clinical patients and 3,510 healthy controls). Most studies were published in the past 5 years using a cross-sectional design, carried out across the world. They were organized by age ranges (18-69 years and 20-89 years), years of schooling, and mental status (with and without mental and behavior disorders). Sixteen of 18 studies had participants aged 18-69 years, and 21 out of 33 studies within the older set suggested that the MoCA is a more sensitive tool for detecting NCD. DISCUSSION AND IMPLICATIONS: Thirty-seven studies suggested that the MoCA is a more sensitive tool for NCD detection because it assesses executive function and visuospatial abilities. Some individuals who demonstrated normal cognitive function on the MMSE had lower performance on the MoCA. However, it seems necessary to establish different cutoffs based on years of schooling to avoid false positives. Future studies should contrast MoCA with other screening tools designed for NCD 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 | 0.034 | 0.149 |
| Meta-epidemiology (narrow) | 0.002 | 0.002 |
| Meta-epidemiology (broad) | 0.013 | 0.009 |
| Bibliometrics | 0.020 | 0.017 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.004 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".