HYPERTENSION AS DETERMINANT OF COGNITIVE DYSFUNCTION AMONG ELDERLY SUB-POPULATION
Bibliographic record
Abstract
Background: Cognitive dysfunction is one of the main impacts of hypertension in the elderly population. Early detection and adequate management of early-stage cognitive dysfunction in hypertensive elderly is expected to improve their cognitive status and quality of life. Objective: This study aimed to determine the association between hypertension and cognitive dysfunction in a sub-population of the elderly in Mataram, Indonesia. Methods: This cross-sectional study involved elderly sub-population recruited consecutively in three public health centers in Mataram, Indonesia. Data included in this study were age, gender, occupation, educational level, hypertension, diabetes mellitus, and cognitive status. Cognitive status was assessed using the Indonesia version of Montreal Cognitive Assessment instrument. Multiple logistic regression analysis was performed to test whether hypertension was a determinant of cognitive dysfunction in participants taking into account the presence of socio-demographic status and diabetes mellitus as another vascular risk factor. Results: This study included 88 elderly as eligible participants. The frequency of cognitive dysfunction among participants was 61.4%. Multiple logistic regression analysis revealed that hypertension was the single variable significantly associated with a high frequency of cognitive dysfunction in elderly sub-population (odds ratio = 3.7; 95% confidence intervals = 1.3 – 10.4; p = 0.014). Conclusion: The frequency of cognitive dysfunction in the elderly sub-population in Mataram was high, amounting to 61.4%. Hypertension was the determinant of this high frequency of cognitive dysfunction in the sub-population studied.
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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 | 0.000 | 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 | 0.000 |
| 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".