Nutritional Status of Patients with Alzheimer’s Disease and Their Caregivers
Bibliographic record
Abstract
BACKGROUND: Malnutrition is one of the most important conditions that negatively affects the health of elder people, particularly in patients with dementia. OBJECTIVE: To provide an assessment of nutritional status of patients affected by Alzheimer's disease (AD) living at home and of their caregivers by means of Mini Nutritional Assessment (MNA), and to explore the influence of different factors on nutrition. METHODS: 90 patients affected by AD living at home and 90 age- and sex-matched caregivers were enrolled. Patients and caregivers, coming from an urban-rural fringe of Southern Italy, were assessed using full MNA, Mini-Mental State Examination, Geriatric Depression Scale- short form, Activity of Daily Living, and Instrumental Activities of Daily Living scales. RESULTS: Malnutrition was found with high prevalence in patients affected by AD of different severity (more than 95% of patients were malnourished or at risk of malnutrition), and associated with reduced functional status. An altered nutrition was also recognized with high rate in the group of caregivers (23.3% were malnourished and 41.1% at risk of malnutrition) and the worse nutritional condition was correlated with higher age and lower functional and cognitive status and education. A positive correlation between MNA score of AD patients and caregivers was found. CONCLUSION: Corrective measures should be taken in order to early identify nutritional deficiencies and risk of malnutrition observed with high rate in both groups of AD patients and their caregivers; in these subjects a nutrition education program and intervention policies are mandatory to restore nutritional status.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 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".