Prevalence of subjective cognitive decline and its association with physical health problems among urban community dwelling elderly population in South India
Bibliographic record
Abstract
INTRODUCTION: No studies in India have explored subjective cognitive decline (SCD), a hallmark of stage II of preclinical Alzheimer's disease. This study aims to assess the prevalence and correlates of SCD in a South Indian, urban, elderly population. METHODS: We screened 403 individuals 60 years of age and older using the Subjective Memory Complaints Questionnaire (SMCQ) and measured objective cognition with the Montreal Cognitive Assessment (MoCA). Physical health parameters were evaluated for all participants. RESULTS: Among the participants, 377 (93.5%) reported subjective memory complaints. Of the 26 individuals without SCD, 15(57.7%) had objective cognitive impairment (MoCA <25). A total of 182 participants (45.2%) were identified with SCD. Higher educational attainment was significantly associated with fewer SCD reports and better cognitive performance (p < 0.001). DISCUSSION: Subjective cognitive decline (SCD) is highly prevalent among older adults. Screening for SCD can help identify individuals at risk for Alzheimer's disease. SCD assessement combined with cost-effective biomarkers that confirms AD will help individuals to be identified for disease-modifying therapies. HIGHLIGHTS: Nearly half of older adults population screened has reported subjective cognitive decline (SCD), highlighting the widespread occurrence of SCD in urban South India. Participants with higher educational attainment had significantly fewer memory complaints and performed better on cognitive assessments. SCD was prevalent even among individuals without major comorbid conditions such as diabetes and hypertension and those who were on regular treatment for metabolic and cardiovascular disorders. Identifying subjective cognitive decline (SCD) can facilitate early and accurate diagnosis of cognitive disorders and help delay progression to dementia. This highlights the importance of developing and implementing improved public health strategies to address these challenges. Further longitudinal studies are necessary to explore the progression of SCD to dementia, focusing on the interplay between cognitive health, biomarkers, and educational factors in the Indian population.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".