Influence of Geriatric Patients’ Food Preferences on the Selection of Discharge Destination
Bibliographic record
Abstract
BACKGROUND: The nonprotein calorie/nitrogen (NPC/N) ratio of food remains poorly investigated. Thus, this study examined the nutritional factors that influence the choice of discharge destination for geriatric patients. METHODS: We retrospectively investigated the patient characteristics, clinical laboratory test results, and hospital food consumption of 65 geriatric patients (80.0 ± 8.2 years; 31 males, 34 females), who were receiving oral nutritional support at a small mixed-care hospital and further explored their discharge destinations. The NPC/N ratios were calculated according to the menus for the meals provided during the first 4 weeks after admission. For logistic regression analysis, the objective variables were discharge destinations (i.e., nursing care facilities including home or medical institutions) whereas the predictor variables were age, sex, nursing care level, hospitalization duration, serum albumin level (Alb), estimated glomerular filtration rate (eGFR), and NPC/N ratio. RESULTS: Compared with age and nursing care level, sex (partial regression coefficient (B) = -5.140, P = 0.002), hospitalization duration (B = 0.077, P = 0.004), Alb (B = 3.223, P = 0.013), eGFR (B = -0.071, P = 0.019), and NPC/N ratio (B = -0.224, P = 0.001) are significantly correlated with the selection of discharge destination. CONCLUSIONS: For geriatric patients who went to medical institutions, the need for prolonged hospitalization, male sex, hospitalization duration, stable serum Alb, low eGFR, low NPC/N ratio (i.e., high protein proportion), and the quantity of hospital food consumed were the possible factors that influence their discharge destination.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.000 |
| 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".