Normative Data of Montreal Cognitive Assessment (MoCA) in Tamil-Speaking Adults
Bibliographic record
Abstract
CONTEXT: Cognitive evaluation to determine possible cognitive decline highlights the need for a thorough neuropsychological assessment for early detection. The Montreal Cognitive Assessment (MoCA), is a commonly used screening tool that is comparatively quick and simple to administer, score, and interpret. Subtests of MoCA assess memory, language, visuospatial functions, and executive functions. AIMS: The present study aims to generate normative data for the Tamil version of the Montreal Cognitive Assessment (MoCA-TAM) in Tamil-speaking adults. DESIGN AND SETTINGS: Cross-sectional study conducted in three districts of Tamil Nadu. METHODS AND MATERIAL: A total of 450 healthy Tamil native speakers with varying ages (21-80 years) and education levels (primary level to university) were recruited as participants. The Tamil version of the Montreal Cognitive Assessment (MoCA-TAM) was used for assessing the cognitive domains. Scores were analyzed to see the impact of age, gender, and years of education on MoCA-TAM scores and individual cognitive domains. STATISTICAL ANALYSIS: Descriptive statistics and Regression analyses were done to evaluate the mean, standard deviation, impact of age, gender, and education on MoCA-TAM scores and individual cognitive domains. RESULTS: The mean value for MoCA-TAM was 24.89 with SD 2.944. MoCA-TAM scores were lower with increasing age and lower education and no statistically significant relationship was found between gender and MoCA-TAM score. CONCLUSIONS: The present study provides the normative data of MoCA-TAM with a single cut-off score (22) to differentiate normal from cognitively impaired.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".