Relationship Between Serum Folic Acid Levels With The Cognitive Function of The Elderly
Bibliographic record
Abstract
Background: Cognitive decline is a common condition that occurs in the elderly. One of the early indicators of senility is a decrease in cognitive function. Folic acid is thought to protect the arteries from damage because of homocysteine by converting homocysteine into cysteine and then excreted in the urine. Increased levels of homocysteine can interfere with vascular function and cause toxic effects on neurons thereby increasing the risk of cognitive decline. Objective: To determine the relationship between serum folic acid levels and cognitive function of the elderly. Method: Analytical descriptive research with a cross-sectional approach. The research subjects were the elderly who met the inclusion criteria and did not have exclusion criteria. The research was conducted from May to July 2022 at the Pucang Gading Nursing Home, Semarang. Serum folic acid levels were examined using the ELISA (Enzyme-linked immunosorbent assay) method. Cognitive function was assessed using the Indonesian version of the Montreal Cognitive Assessment (MoCA) simultaneously on the subject. Cognitive function is normal if the MoCA-INA value is ≥ 26 and it is said to be cognitive dysfunction if the MoCA-INA value is < 26. Data were analyzed using the Spearman test. Results are considered significant if the value of p <0.05. Result: There is a strong positive correlation between serum folic acid levels and cognitive function in the elderly (r=0.914, p<0.001). There is a relationship between educational level and cognitive function (r=0.922, p<0.001) where higher education correlates with increased cognitive function in the elderly. Conclusion: There is a significant positive correlation between serum folic acid levels and cognitive function in the elderly
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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 | 0.001 | 0.003 |
| 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) | 0.001 | 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".