Normative Scores for Malayalam Cognitive Screening Test Battery among Older Adults in Kerala, India
Bibliographic record
Abstract
Background: Many neuropsychological tests are primarily developed in high-income countries, and normative data are not readily available for low- and middle-income countries (LMICs). We need culturally appropriate cognitive screening tests for India. Objective: Hence, we decided to translate the Hindi cognitive screening test battery (HCSTB) tool to Malayalam and establish the age and education-stratified norms for a Malayalam cognitive screening test battery (MCSTB). Material and Methods: HCSTB was translated to Malayalam, back-translated by bilinguals conversant in Malayalam and English, and pretested on 30 older normal adults. Using a multistage sampling technique, we conducted a descriptive cross-sectional survey in the Thiruvananthapuram district of Kerala, India. We approached older adults aged ≥60 years for informed and written consent. We excluded subjects with depression, functional impairment, cognitive impairment, history of stroke, psychosis, and visual/hearing loss that impaired cognitive assessment. Results: The normative data were derived from 441 older adults: 226 (51%) from rural areas and 215 (49%) from urban areas. Age and education affected the cognitive scores. The time to administer MCSTB among normal adults was approximately 17 minutes. Discussion and Conclusion: The derived normative data showed lower values than the published literature. A limitation of our study was the small number of older people with ≥12 years of education and the lack of neuroimaging of the subjects.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".