Prevalence of dementia in elderly age population of Barangay Bangkal, City
Bibliographic record
Abstract
ABSTRACT Background Dementia, a significant cause of disability and dependency among older adults. The growing population of the elderly in the Philippines is expected to increase the prevalence of dementia in the country. Purpose This study aims to determine the prevalence of dementia in the elderly population of Barangay Bangkal, Makati City. Methods Descriptive cross-sectional community-based study was conducted to determine the prevalence of dementia in the elderly population of Barangay Bangkal, Makati City, aged 60 years and above over one month from mid-October to mid-November 2019. Data was collected with help of Mini-Mental State Examination – Philippines version (MMSE-P) to determine the cognitive status and diagnose dementia in elderly population. Results A total of 266 elderly adults participated in the study. Representatives of the study population were male (59.0%), married (68.0%), with an income of less than 5,000 peso (51.1%), working (64.3%), and with high school education (42.1%). The average age of the study population was 68.02 ( + 6.76) years. The average MMSE score of the participants was 27.05 ( + 3.94). The prevalence of dementia in the sample was 18.8%. Age, income, and level of education were associated with the MMSE score ( r □ = - 0.26, n = 266, p < 0.001, r □ = 0.23, n = 266, p < 0.001, and rs = 0.19, n = 266, p = 0.002, respectively). The findings for statistical significance do resonate with clinical significance as evident during administration of MMSE score. Conclusion Advancing age increases the risk for cognitive decline while higher income and education level prevent or delays the onset of dementia. Collaborative management between the medical education faculty & students, researchers, and local state health officials might address dementia in the region.
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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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".