Establishing a Dementia Prevention Framework in Rural India: The SMRUTHI Cohort's Baseline View
Bibliographic record
Abstract
BACKGROUND: With the projected surge in dementia cases across low- and middle-income countries, India faces an urgent need for early prevention strategies targeting at-risk populations. The SMRUTHI cohort was developed to characterize dementia risk factors and cognitive health among rural elderly individuals (aged ≥55 years) across diverse Indian regions. METHOD: This multicentric, cross-sectional study recruited 2,402 participants across four rural regions: Tripura (East), Rajasthan (West), Karnataka (South), and Himachal Pradesh (North). Trained field investigators and psychologists conducted home visits and collected data using validated Case Report Forms (CRFs) via the REDCap platform. A dedicated data management team performed daily checks to ensure completeness and consistency. Participants were classified as illiterate if they were unable to read and write a short, simple statement related to daily life. Missing data were minimized through mandatory REDCap fields, planned revisits, and reassurances about confidentiality. Multiple imputation by chained equations (MICE) was planned for any remaining missing data. Monthly backups were done by the central statistician to ensure data security. Blinding was maintained; outcome assessors and the statistical analyst remained unaware of group allocation during analysis. RESULT: Among the 2,402 participants, most were aged ≥60 years (75.4-81.5%), and females comprised 58-66% across sites. Illiteracy was highest in Tripura (81.8%) and Karnataka (77.8%). Hypertension and diabetes were observed in up to 40.2% and 16.6%, respectively. Smoking (41.1%) and alcohol use (41.3%) were highest in Tripura. High adherence to the MIND diet was found in 97.3% in Tripura, but only 17.9% in Rajasthan. Low physical activity (<600 MET-min/week) was most common in Karnataka (58.6%). PHQ-9 scores showed depressive symptoms ≥5 in up to 9.7% (Karnataka); non-zero medians ranged from 1 (Himachal) to 6 (Tripura). Median ACE-III scores varied by region: Tripura 78 (95% CI: 77-80), Rajasthan 84 (83-85), Karnataka 88 (88-89), and Himachal 87 (87-88). CONCLUSION: The SMRUTHI cohort provides critical baseline data on dementia risk in rural India, highlighting the need for region-specific, multidomain prevention strategies in low-resource settings.
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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 | 0.005 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".