Cognitive Screening Tools for Dementia Detection in Primary Healthcare Centers in India: A Scoping Review
Bibliographic record
Abstract
BACKGROUND: Dementia is a growing public health concern in India, with an increasing prevalence among the elderly population. Early detection is crucial for effective intervention. Primary healthcare (PHC) centres play a vital role in identifying cognitive impairment; however, the effectiveness of cognitive screening tools in these settings is questionable. OBJECTIVE: This scoping review explores the cognitive screening tools available for dementia detection in PHC centres in India, assesses their effectiveness, and identifies the need for their improvement and adaptation. METHODS: A systematic search was conducted in PubMed, Scopus, and Google Scholar for studies published up to October 2024. A total of 29 studies were identified, indicating that tools such as the Mini-Mental State Examination (MMSE) and the Montreal Cognitive Assessment (MoCA) are frequently used. However, these tools face significant challenges related to educational background and language comprehension, impacting their effectiveness. CONCLUSION: There is an urgent need for culturally and linguistically appropriate cognitive screening tools in PHC settings in India to enhance the early detection of dementia.
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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 | 0.011 | 0.049 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.006 |
| Bibliometrics | 0.015 | 0.014 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".