Normative Data for the Montreal Cognitive Assessment in Greek Older Adults With Subjective Cognitive Decline, Mild Cognitive Impairment and Dementia
Bibliographic record
Abstract
OBJECTIVE: The aim of the study was to provide normative data for the MoCA in a Greek cohort of people older than 60 years who meet criteria for subjective cognitive decline (SCD), mild cognitive impairment (MCI), or dementia in order to optimize cutoff scores for each diagnostic group. METHOD: Seven hundred forty-six community-dwelling older adults, visitors of the Day Center of Alzheimer Hellas were randomly chosen. Three hundred seventy-nine of them met the criteria for dementia, 245 for MCI and 122 for SCD. RESULTS: < .001). A cutoff score of 23 for low educational level (≤6 years) can distinguish people with SCD from MCI (sensitivity 71.4%, specificity 84.2%), while 26 is the cutoff score for middle educational level (7-12 years; sensitivity 73.2%, specificity 67.0%) and high educational level (≥13 years; sensitivity 77.6%, specificity 74.7%). Montreal Cognitive Assessment can discriminate older adults with SCD from dementia, with a cutoff score of 20 for low educational level (sensitivity 100%, specificity 92.3%) and a cutoff score 23 for middle educational level (sensitivity 97.6%, specificity 92.7%) and high educational level (sensitivity 98.5%, specificity 100%). CONCLUSION: Montreal Cognitive Assessment is not affected by age or gender but is affected by the educational level. The discriminant potential of MoCA between SCD and MCI is good, while the discrimination of SCD from dementia is excellent.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".