To Establish Normative Data of Montreal Cognitive Assessment Test (MoCA) Scale in Middle-Aged Adults (40-60 years) of Indian Population Residing in Mumbai - A Cross-Sectional Study
Bibliographic record
Abstract
Background: Cognitive decline is among the most feared aspects of growing old. Assessing cognitive functioning is important in determining the level of impairment. Montreal Cognitive Assessment Test (MoCA) has been used as a rapid screening tool to assess cognitive function. Objective: This study was conducted to establish normative data in the adult population of Mumbai (40-60 years of age) using the MoCA scale. Materials and methods: The Institutional Ethics Committee (IEC) approval was obtained and a cross-sectional study was conducted in a metropolitan city in India, from November 2022 to December 2022. Adults were screened using The Saint Louis University Mental Status Examination (SLUMS) out of which 107 participants were recruited, ranging from 40-60 years of age. Montreal cognitive assessment test (MoCA) was used to capture normative data on the age, gender, and education demographics. Results: The composite mean score of MoCA was found as 24.12 with a standard deviation of ±3.525 giving a range of 20.595 - 27.645. There was no significant difference in despite of scores between the two age groups and the gender variant. However, education had an impactful difference. The mean score on MoCA for graduates and primary education was 25.39 and 21.80 respectively. It showed a statistically highly significant difference with higher values in graduates. Conclusion: The MoCA scores were directly correlated with educational status. Hence, this study helped us to understand the subtle cognitive impairments in early adulthood. Population-based normative data is essential for early screening and diagnosis. Key words: Montreal cognitive assessment test MoCA, Normative data, Middle-aged adults, cognitive assessment.
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 | 0.014 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".