Cognitive impairment and its predictors: A cross-sectional study among the elderly in a rural community of West Bengal
Bibliographic record
Abstract
CONTEXT: With the aging of Indian society, maintaining salubrious cognitive health in late life is a public health priority. Early detection and possible prevention of cognitive impairment (CI), thus, will help in increasing the quality of life of elderly people and decreasing the social, psychological, and economic burden of their families and caregivers. AIMS: The study aimed to assess proportion of CI and its predictors. SETTINGS AND DESIGN: This community-based cross-sectional study was conducted among 135 elderly people selected from 15 villages out of a total 64 villages in rural field practice area Singur of AIIH&PH, Kolkata. METHODS AND MATERIAL: Cluster sampling technique was used and villages were selected according to probability proportional to size method. Data was collected using a predesigned, pretested structured schedule, which included sociodemographic and behavioral variables, Montreal cognitive assessment tool, Geriatric depression scale short form (GDS 15), and mini nutritional assessment tool. STATISTICAL ANALYSIS USED: Predictors of CI were assessed by univariate and multivariable logistic regression using MS-Excel 2016 and SPSS version 16 software. RESULTS: Mean age of the study participants was 67.03 ± 6.7 years with 51.9% of them being females. Proportion of CI was observed to be 48.1% which was significantly associated with increasing age [AOR = 1.1 (1.02-1.13)], decreasing years of schooling [AOR = 1.1 (1.01-1.2)], depression [AOR = 2.7 (1.3-5.8)], and malnourished group [AOR = 4.5 (1.01-20.3)]. CONCLUSION: The burden of CI among the study population was found to be quite high. It is an alarming situation which needs improved screening facility for early detection. Nutritional upliftment and screening for depression should also be done on a regular basis.
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How this classification was reachedexpand
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".