Cognitive Frailty in Thai Community-Dwelling Elderly: Prevalence and Its Association with Malnutrition
Bibliographic record
Abstract
Cognitive frailty (CF) is defined by the coexistence of physical frailty and mild cognitive impairment. Malnutrition is an underlying factor of age-related conditions including physical frailty. However, the evidence associating malnutrition and cognitive frailty is limited. This cross-sectional study aimed to determine the association between malnutrition and CF in the elderly. A total of 373 participants aged 65-84 years were enrolled after excluding those who were suspected to have dementia and depression. Then, 61 CF and 45 normal participants were randomly selected to measure serum prealbumin level. Cognitive function was assessed using the Montreal Cognitive Assessment-Basic (MoCA-B). Modified Fried's criteria were used to define physical frailty. Nutritional status was evaluated by the Mini Nutritional Assessment-short form (MNA-SF), serum prealbumin, and anthropometric measurements. The prevalence of CF was 28.72%. Malnourished status by MNA-SF category (aOR = 2.81, 95%CI: 1.18-6.67) and MNA-SF score (aOR = 0.84, 95%CI = 0.74-0.94) were independently associated with CF. However, there was no correlation between CF and malnutrition assessed by serum prealbumin level and anthropometric measurements. Other independent risk factors of CF were advanced age (aOR = 1.06, 95%CI: 1.02-1.11) and educational level below high school (aOR = 6.77, 95%CI: 1.99-23.01). Malnutrition was associated with CF among Thai elderly. High-risk groups who are old and poorly educated should receive early screening and nutritional interventions.
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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 | 0.001 | 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".