Validating the Telephone Montreal Cognitive Assessment Scale–Hindi as a Remote Screening Tool for Mild Cognitive Impairment
Bibliographic record
Abstract
BACKGROUND: Mild cognitive impairment (MCI) is a neurocognitive disorder that adversely affects cognitive function and is often a precursor to dementia. Early diagnosis of MCI can guide timely treatment and delay dementia onset. The Montreal Cognitive Assessment (MoCA) is an effective screening tool for MCI. Remotely administered versions of the MoCA have gained popularity during the recent pandemic. OBJECTIVE: This study aimed to validate a new Hindi version of the Telephone MoCA (T-MoCA) in older adult outpatients at a tertiary hospital in Delhi, India. METHODS: We developed and validated a Hindi version of the T-MoCA (T-MoCA-Hindi) using the previously validated MoCA-Hindi as the gold standard. We administered both instruments to older adult patients with a 2-week gap between sessions. To assess the reliability of the new T-MoCA-Hindi, we used the Bland-Altman plot and Cronbach alpha. We used a receiver operating characteristic curve to estimate accuracy. RESULTS: A total of 243 individuals enrolled in this study. The T-MoCA-Hindi and the MoCA-Hindi showed a high level of agreement. A Cronbach alpha of 0.84 indicated good internal consistency. The area under the curve in the receiver operating characteristic analysis was 93.5%, indicating excellent accuracy and validity and demonstrating high sensitivity and specificity at an optimal cut-off score of 18/19 points. CONCLUSION: These findings show that the T-MoCA-Hindi is a valid tool for remote identification of MCI in India. The use of remote versions of diagnostic tools can be leveraged to conduct research when in-person approaches may not be feasible.
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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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| 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".