Construct Validity and Psychometric Properties of the Tamil (India) Version of Montreal Cognitive Assessment (T-MoCA) in Elderly
Bibliographic record
Abstract
Background: The Montreal Cognitive Assessment (MoCA) is a neuropsychological cognitive tool developed and adapted widely in various languages for screening mild cognitive impairment (MCI). Objectives: The present study aimed to evaluate the psychometric properties of the Tamil (India) Version of MoCA (T-MoCA) and further examine the construct validity of the tool.Method: The authors conducted internal consistency, test-retest, sensitivity-specificity, and construct validity using 233 Tamil-speaking elderly participants. The inclusion criteria of the study participants were 0.5 or less than 0.5 scores in the Clinical Dementia Rating scale (CDR). Further, T-MoCA was used to screen MCI. Results: The result showed that the T-MoCA had high internal consistency (0.83) and high test-retest reliability (0.92). Receiver operating characteristic (ROC) analyses showed an area under the curve (AUC) of 0.91 (95% CI 0.87-0.94) for detecting MCI. Furthermore, the optimal cut-off score to detect MCI was 24, accommodated a sensitivity and specificity of 88.4% and 77.9%, respectively. Conclusions: The Tamil (India) version of the MoCA maintained its core diagnostic properties, furnishing it a valid and reliable tool for the screening of MCI. Also, its latent dimensions help to understand the elders’ cognitive function in a better way.
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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 | 0.006 | 0.023 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".