Nutritional Intake by Meal Time Zone in Geriatric Patients Is Related to Nutritional Assessment Index
Bibliographic record
Abstract
BACKGROUND: The blood metabolome profiles depend on the meal intake time zone regardless of having the same meal. The serum albumin (Alb) level, which is important in managing geriatric patients with chronic diseases, is included in the metabolome analysis. In this study, we aimed to examine the relationship between Alb and the nutritional value of hospital meals consumed at breakfast, lunch, and dinner among geriatric patients. Chrononutrition was considered while drawing inferences. METHODS: We retrospectively surveyed 52 geriatric patients with chronic diseases (aged 79.7 ± 8.7 years) admitted at a small-scale hospital providing combined healthcare measures and oral nutritional support. The dietary intake per kilogram of body weight of nutritional components for breakfast, lunch, and dinner was individually expressed as the ratio to the whole daily food intake. The dietary pattern was determined by principal component analysis. We also conducted linear regression analysis, with Alb as the dependent variable, and age, sex, and grade assigned in this study as well as the first, second, and third principal components of the dietary patterns as the independent variables. RESULTS: Three principal components with an eigenvalue of > 1 were extracted. The second principal component was a significantly negative determinant factor for Alb (B = -0.108, P = 0.016). In patients with high Alb levels, the energy, protein, and fat ratios at lunch were positively correlated, while the energy and carbohydrate ratios at dinner were negatively correlated. Mealtimes were fixed. CONCLUSIONS: The results of this study showed that the dietary pattern predominantly observed in patients with high Alb levels may be positively associated with Alb synthesis.
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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 | 0.000 | 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".