MoCA in five Indian languages: A brief screening tool to diagnose dementia and MCI in a linguistically diverse setting
Bibliographic record
Abstract
INTRODUCTION AND OBJECTIVES: Early dementia diagnosis in low and middle-income countries (LMIC) is challenging due to limited availability of brief, culturally appropriate, and psychometrically validated tests. Montreal Cognitive Assessment (MoCA) is one of the most widely used cognitive screening tests in primary and secondary care globally. In the current study, we adapted and validated MoCA in five Indian languages (Hindi, Bengali, Telugu, Kannada, and Malayalam) and determined the optimal cut-off points that correspond to screening for clinical diagnosis of dementia and MCI. METHODS: A systematic process of adaptation and modifications of MoCA was fulfilled. A total of 446 participants: 214 controls, 102 dementia, and 130 MCI were recruited across six centers. RESULTS: Across five languages, the area under the curve for diagnosis of dementia varied from 0.89 to 0.98 and MCI varied from 0.73 to 0.96. The sensitivity, specificity and optimum cut-off scores were established separately for five Indian languages. CONCLUSIONS: The Indian adapted MoCA is standardized and validated in five Indian languages for early diagnosis of dementia and MCI in a linguistically and culturally diverse population.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.002 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 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".