Optimal Cutoff Scores for Dementia and Mild Cognitive Impairment of the Montreal Cognitive Assessment among Elderly and Oldest-Old Chinese Population
Bibliographic record
Abstract
BACKGROUND: All versions of the Montreal Cognitive Assessment (MoCA) lack population-based data of 80-plus individuals. The norms and cut-off scores for mild cognitive impairment (MCI) and dementia of the MoCA are different among five Chinese versions. OBJECTIVE: To provide the cut-off scores in detecting MCI and dementia of the Peking Medical Union College Hospital version of the MoCA (MoCA-P). METHODS: In a cross-sectional survey, Chinese veterans aged ≥60 years completed the MoCA-P and the Mini-Mental State Examination (MMSE). RESULTS: Among 7,445 elderly veterans, 5,085 (68.30%) were aged ≥80 years old, 2,621 (35.20%) had 6 years of education or less, 6,847 (91.97%) were male, and 2,311 (31.04%) and 984 (13.22%) veterans were diagnosed as having MCI and dementia, respectively. Adding two points and one point to the MoCA scores for the primary and middle school groups, respectively, can fully adjust for the notable impact of education but cannot compensate for the effect of age. In the three age groups (60-79, 80-89, and ≥90 years old), the optimal MoCA-P cut-off scores for detecting MCI were ≤25, ≤24, and ≤23, respectively, and for detecting dementia were ≤24, ≤21, and ≤19, respectively, which demonstrated relatively high sensitivities and specificities. The areas under the curves for the MoCA-P for detecting MCI and dementia (0.937 and 0.908, respectively) were greater than those for the MMSE (0.848 and 0.892, respectively). CONCLUSION: Compared with the MMSE, the MoCA-P is significantly better for detecting MCI in the elderly, particularly in the oldest old population, and it also displays more effectiveness in detecting dementia.
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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 | 0.003 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".