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 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 | 0.001 | 0.006 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".