Validation of the Czech Montreal Cognitive Assessment for Mild Cognitive Impairment due to Alzheimer Disease and Czech Norms in 1,552 Elderly Persons
Bibliographic record
Abstract
BACKGROUND: The Czech version of the Montreal Cognitive Assessment (MoCA-CZ) and delayed recall of 5 words have not been validated in patients with mild cognitive impairment (MCI) due to Alzheimer disease (AD) and compared to norms of a large population. METHOD: The MoCA-CZ was administered to 1,600 elderly individuals in 2 groups consisting of 48 patients with MCI due to AD (AD-MCI) and 1,552 normal elderly adults. RESULTS: MoCA-CZ scores were significantly lower in the AD-MCI patients than in the normal elderly (21 ± 4 vs. 26 ± 3 points; p = 0.03). Under the recommended cutoff score of ≤25, the MoCA-CZ demonstrated an excellent sensitivity of 94% but a low specificity of 62%. When the score was reduced to ≤24, the MoCA-CZ showed an optimal sensitivity of 87% for AD-MCI and a specificity of 72%. Normal elderly persons should recall at least 2 words after delay (sensitivity 80%, specificity 74%). Several cutoff points were derived from normative data stratified by age and education. CONCLUSIONS: The cutoff for AD-MCI and stratified norms are available for the MoCA total score and delayed recall of the Czech version. The cut-off scores of the MoCA-CZ, sensitivity, and specificity are lower than in the original study.
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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 | 0.004 | 0.011 |
| 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 | 0.001 |
| 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".