Montreal Cognitive Assessment (MoCA): Normative Data for the State of Kerala, South India
Bibliographic record
Abstract
BACKGROUND: Montreal cognitive assessment (MoCA) is a tool that is widely accepted across the world to measure mild cognitive impairment (MCI). The original cut-off score of MoCA falsely screens a large population of Indians as having MCI. OBJECTIVE: The aim of this study was to develop the normative data for MoCA for the older population of Kerala, South India. MATERIAL AND METHODS: We conducted the study among 959 cognitively normal older individuals of Kalliyoor village of Thiruvananthapuram district, Kerala. The validated Malayalam version of MoCA [MoCA-M] was administered by trained volunteers. The mean, median, and 10th percentile of the scores [domain-specific and total] were calculated in various age and educational groups. RESULTS: The mean (SD) MoCA score was 19.4 (7.3). The 10th percentile for the total MoCA score was 9. The 10th percentile for all domains was zero, except for orientation. As age advanced, MoCA scores significantly reduced. The mean total MoCA scores dropped from 20.1 (7) [for ages between 65 and 75 years] to 7.4 (1.6) [for ages above 85 years]. We also obtained a significant improvement in scores among subjects with higher educational standards. CONCLUSION: The study throws light into the performance of MoCA among the Indian population. This study defines the norms for the Indian population and suggests redefining the threshold for positively screening for MCI using MoCA-M.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".