Comparison of the Montreal Cognitive Assessment and the Mini-Mental State Examination in screening diagnostics of Alzheimer's disease
Bibliographic record
Abstract
This thesis deals with the neuropsychological diagnosis of Alzheimer's disease. The aim is to evaluate the psychometric characteristics ofthe new Czech translation of the Montreal Cognitive Assessment (MoCA) by comparison with the Mini-Mental State Examination (MMSE), a method widely used by doctors inscreening diagnostics of Alzheimer's disease. The theoretical part deals with the diagnostics of Alzheimer's disease and mild cognitive impairment. We describe international diagnostic criteria of cognitive disorders and provide an overview of the screening neuropsychological methods most commonly used by Czech specialists. We summarize the current psychometric and psychodiagnostic findings on these methods and focuse on description of MMSE and MoCA. In the empirical part we compare Czech version of MMSE and MoCA-CZ (the new Czech translation of the test). We examined 38 patients with Alzheimer's disease and 70 cognitively healthy seniors. The results show that MoCA-CZ is sufficiently valid and reliable screening method that accurately distinguishbetween healthy subjects and patiens with Alzheimer's disease. We believe that it can enrich screening tools that are available to Czech experts. Key words: Montreal Cognitive Assessment, Mini-Mental State Examination, Alzheimer's disease, psychodiagnostics,...
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.017 | 0.049 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".