Cognitive Screening Tools for Dementia Detection in Primary Healthcare Centers in India: A Scoping Review
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
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.
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.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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 it