[Hungarian version of the Montreal Cognitive Assessment (MoCA) for screening mild cognitive impairment].
Bibliographic record
Abstract
Mild cognitive impairment (MCI) can be considered as an intermediate stage between normal cognitive aging and dementia. Its screening is extremely important because within a year in 15-20% of cases dementia can evolve. In Hungary, the most widely used screening tool for both dementia and MCI is the Mini Mental State Examination (MMSE), which is often criticized for its poor screening sensitivity of mild dementia and MCI. To eliminate this problem, the Montreal Cognitive Assessment (MoCA) was developed, especially for screening MCI. Our study presents the first results with the Hungarian translation of MoCA. We used Beck Depression Inventory (BDI) for controlling depression. In MoCA the cutoff score between healthy and MCI persons was 24 out of 30. MoCA was more sensitive in detecting MCI than MMSE and its inner consistency was also slightly higher. Specificity of the tests to detect MCI was similar. The results on BDI were not related to either MoCA or MMSE. Our results suggest that MoCA can be a useful tool to detect cognitive decline.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.011 | 0.004 |
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".