ASSOCIATION OF LIPID PROFILES AND COGNITIVE FUNCTION DETERIORATION OF GERIATRIC OUTPATIENTS AT NEUROLOGY CLINIC GUNUNGSITOLI REGIONAL GENERAL HOSPITAL
Bibliographic record
Abstract
Background: The increase of life expectancy in Indonesia causes increasing numbers of dementia, mostly caused by Alzheimer's disease (AD). High serum cholesterol level has been suggested as a risk factor for AD. The strongest evidence linking lipid profile with AD provided by previous experimental studies where adding or reducing cholesterol altered amyloid precursor protein (APP) and amyloid ?-protein (A?) levels is the basic of this research. Objective: To determine whether lipid profile is associated with cognitive function deterioration of geriatric outpatients at Neurology Clinic Gunungsitoli Regional General Hospital. Methods: Participants of this cross-sectional study were outpatient geriatric patients at Neurology Clinic Gunungsitoli Regional General Hospital (n=85; mean age, 62.46±5.49 years old). The cognitive state was evaluated using Montreal Cognitive Assessment Indonesian Version (MoCA-INA) and those with MoCA-INA score <24 were considered cognitively declined. Concentrations of serum lipid profile were measured and correlated with cognitive state using Pearson’s correlation. Multiple logistic regression analysis was used to calculate odds ratios (ORs) for cognitive decline. Results: Based on Pearson’s correlation test, high-density lipoprotein (HDL) level had significant strong positive correlation with MoCA-INA score (r=0.876;p=0.000) and triglyceride level had significant strong negative correlation with MoCA-INA score (r=-0.726;p=0.000). Relatively to cognitive decline, ORs for decreased HDL level was 3.19 (95%CI 2.02-4.36) and increased triglyceride level was 2.59 (95%CI 1.29-3.91). Conclusion: There was a significant relationship between decreased HDL-cholesterol level and increased triglyceride level with cognitive decline in geriatric outpatients.
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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 | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".