Cognitive Impairment and Its Associated Determinants Among the Elderly Population of Telangana, India: An Analytical Prevalence Study
Bibliographic record
Abstract
INTRODUCTION: Dementia is an insidious cognitive disorder featuring a decline in cognition that is not well explained by the physiology of aging. Dementia includes a group of disorders that are distinguished by a gradual loss of both cognition and the capability to execute day-to-day functions. MATERIALS AND METHODS: We conducted a cross-sectional study among 384 elderly participants in areas surrounding the All India Institute of Medical Sciences, Bibinagar, Telangana, India. Those with more than 65 years of age were included in the study, and those suffering from serious illnesses were excluded. The Montreal Cognitive Assessment (MOCA) scale, the University of California and Los Angeles (UCLA) Loneliness Scale, and the Patient Health Questionnaire (PHQ-9) were used to assess cognitive status, loneliness, and depression, respectively, among the study participants. Logistic regression was performed to identify factors associated with cognitive impairment (CI), depression, and loneliness. RESULTS: The average MOCA score of the study participants was 14.9 ± 6.9, with 28.6% of the participants exhibiting severe CI. Nearly half of the participants (49.2%) experienced moderate to high degrees of loneliness, and 39.3% experienced moderate to severe depression. Important factors found to be associated with severe CI were illiteracy (adjusted odds ratio (AOR): 2.85, 95% CI: 1.35-4.45), urban residence (AOR: 0.18, 95% CI: 0.04-0.81), living with a spouse (AOR: 0.23, 95% CI: 0.11-0.78), not consuming alcohol (AOR: 0.35, 95% CI: 0.14-0.87), and depression (AOR: 4.49, 95% CI: 1.37-14.67). CONCLUSION: CI is a serious public health problem in India. With the increasing proportion of the elderly population in the near future, CI levels will increase, especially in countries like India. Timely interventions such as early identification through community-based screening, the inclusion of a geriatric health component in primary health care, and proper counseling will help address this problem at a grassroots level.
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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 | 0.002 |
| Science and technology studies | 0.001 | 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".