Validation of the Slovenian version of the Montreal Cognitive Assessment Scale as a screening tool for the detection of mild cognitive impairment
Bibliographic record
Abstract
OBJECTIVE: The Montreal cognitive assessment scale (MoCA) is commonly used for detecting individuals with mild cognitive impairment (MCI). The aim of the present study was to evaluate the validity of the Slovenian MoCA as a screening tool for MCI and to determine the optimal cut-off point to detect MCI in the elderly population. METHODS: Mini-Mental State Examination (MMSE), MoCA, and neuropsychological testing assessment were conducted on 93 individuals aged ≥ 60 years. MCI was found in 35 individuals with 58 cognitively asymptomatic controls. Cut-off values, sensitivity, and specificity of MoCA were calculated with the receiver operating characteristic curve. RESULTS: MCI and healthy individuals did not differ with respect to age and education. Healthy individuals (M = 24.5, SD = 1.7) performed significantly better on MoCA compared to MCI individuals (M = 21.4, SD = 3.2) (p < 0.001). The Cronbach's α of MoCA as an index of internal consistency was 0.64. MoCA distinguished between healthy controls and MCI individuals with a sensitivity of 77% and specificity of 74%, using a cut-off of 23/24 points. CONCLUSION: The Slovenian version of MoCA demonstrates an optimal cut-off value of 23/24 points for detecting older individuals with MCI. As a screening tool for MCI, its better diagnostic accuracy makes it preferable to using MMSE.
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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 | 0.013 | 0.017 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".