Evaluating cognitive impairment among a geriatric population in India using the Indian Council of Medical Research (ICMR)–multilingual dementia research and assessment (MUDRA) toolbox
Bibliographic record
Abstract
Background: Cognitive impairment disproportionately affects the geriatric population in India due to a combination of nutritional, socioeconomic, and environmental factors. Aim: This study employed the culturally validated Indian Council of Medical Research (ICMR)–Multilingual Dementia Research and Assessment (MUDRA) Toolbox to investigate cognitive impairment among geriatric population in Gorakhpur, India and to assess their performance in the various cognitive domains of the MUDRA toolbox. Methods: This cross-sectional study included 1013 participants aged over 60 years selected through multistage random sampling across seven blocks in Gorakhpur district. Participants who exhibited cognitive impairment on the Montreal Cognitive Assessment (MoCA) test underwent further assessments across multiple cognitive domains in the MUDRA Toolbox, including tests of attention and executive functions, episodic memory, language, and visuospatial functions. Data were analyzed using descriptive statistics, Chi-square test, Mann–Whitney U test, and multinomial logistic regression. Results: Among 1013 participants, 847 screened positive on MoCA (70.4% mild, 13.2% moderate) for cognitive impairment and were assessed further for other domains of ICMR-MUDRA toolbox. Males performed significantly better than females across multiple MUDRA toolbox domains, including attention and executive functions, episodic memory, language, and visuospatial skills ( P ≤ 0.05). Females showed higher error rates in Trail Making Tests and higher line bisection deviation ( P < 0.001). Conclusion: Significant cognitive impairment exists among geriatric population in Gorakhpur. It could arise from the disparities in educational attainment and occupational engagement. These findings emphasize the need for early public health interventions specific for middle-aged population to delay the onset of cognitive impairment.
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 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 | 0.036 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| 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".