Random blood glucose level as predictor of cognitive impairment in elderly
Bibliographic record
Abstract
Background Nutritional deficits have been linked to poor cognitive function and are highly prevalent in the elderly. Several factors associated with cognitive function have been studied, but the results were inconclusive. The objective of this study was to determine the relationship between blood glucose level and cognitive impairment in the elderly. Methods A cross-sectional study was conducted and a total of 109 elderly were included in the study. Research subjects were selected using consecutive non-random sampling from the Tebet sub-district in South Jakarta. Random blood glucose level was assessed using glucose strips (Nesco). Cognitive function was measured with the Montreal Cognitive Assessment (MoCA) and Informant Questionnaire on Cognitive Decline in Elderly (IQCODE) questionnaire. The relationship between blood glucose levels and cognitive function was analyzed by means of multiple linear regression analysis. Results The mean age of the elderly was 67.95 ± 6.42 years, length of formal education was 10.12 ± 5.88 years, and mean random blood glucose level was 137.41 ± 70.25 mg/dL. Multiple regression analysis showed that length of formal education (â= 0.769; p=0.000) and random blood glucose levels (â=0.016; p=0.014) were significantly associated with cognitive function. Conclusion Cognitive function is negatively affected by high blood glucose, thus random blood glucose level can be used to predict cognitive impairment.
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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) | 0.001 | 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".