Prevalence of subjective cognitive decline and its association with physical health problems among urban community dwelling elderly population in South India
Bibliographic record
Abstract
Abstract Background The incidence of cognitive disorders is increasing in India1. Stage II of the preclinical Alzheimer’s Disease (PCAD) is characterised by subjective complaints of decline in cognition, termed Subjective Cognitive Decline (SCD)2,3. Objective assessments of cognition is normal in that stage. There are no studies from India that looked at SCD in the community. Our study aims to look at the prevalence of SCD in an urban dwelling elderly population and its correlates. Method 418 individuals above the age of 60 were screened using Subjective Memory Complains Questionnaire (SMCQ) and also screened for medical and psychiatric illness. Objective measurement of cognitive functions was done using MOCA. Results 372(92.5%) had reported at least 1 subjective complaint using the participant version of the Subjective Memory Complaint Questionnaire(SMCQ). Among those without any reported subjective complaints,17(56.6%) had objective cognitive impairment on assessments. In total,13 (3.49%) had normal cognition and 179(48.12%) of the evaluated subjects had pre‐MCI SCD. There is no statistically significant association between physical health parameters of Diabetes mellitus and Hypertension or any sociodemographic variables except educational attainment. The mean subjective complaints score is lesser and means objective cognition scores are higher in those with higher secondary school educational attainment (p value<0.001) when compared to those who don’t. Conclusions There is significant morbidity of cognitive decline in the elderly. Subjective memory complaints are present in most of the elderly(92.5%) and 179(48.12%) had pre‐MCI SCD. SCD is a complex condition that requires further longitudinal studies with biomarker assessment to understand the mechanism of progression to AD.
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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".