Використання Монреальського когнітивного тесту для діагностики когнітивних порушень у практиці лікаря-невролога (огляд літератури)
Bibliographic record
Abstract
The Montreal Cognitive Assessment (MoCA) is widely used in clinical practice and in both academic and non-academic research worldwide (available in approximately 100 languages and dialects). It provides the most accurate assessment of cognitive functions and is a reliable method for diagnosing mild cognitive impairment (MCI) or early signs of dementia, including in neurologists’ and general practitioners’ practices. Unlike the Mini-Mental State Examination (MMSE), which, at the time of its development, did not aim to diagnose mild cognitive impairment or detect early stages of dementia, the MoCA was specifically designed in 1995 to identify mild cognitive impairment. Studies have shown that the MoCA has greater predictive accuracy compared to the MMSE for diagnosing both mild cognitive impairment and dementia. The test requires only 10 minutes to complete. The MoCA has a stable hierarchical factor structure with a general factor at the top and satisfactory general factor loading with measurement invariance among participants of different ages, education levels, economic statuses, and genders. Vascular cognitive impairments are characterized by deficits in executive functions, which are essential for cognitive processes such as initiation, planning, hypothesis formation, cognitive flexibility, decision-making, regulation, judgment, feedback, and perceptual body awareness. A comprehensive assessment of executive functions can be conducted using the MoCA. Currently, there is no universally accepted classification for the severity of cognitive impairments based on MoCA test results, but most studies use the gradations proposed by the test developers, which include normal cognitive function, mild cognitive impairment, and dementia. This differentiation helps physicians determine the level of impairment and adjust treatment or therapeutic approaches accordingly. As a result, the MoCA is becoming an increasingly popular tool in clinical practice, especially among neurologists, for early diagnosis and monitoring the progression of cognitive impairments.
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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 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.005 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.039 | 0.016 |
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".