Nutritional Interpretation of Hospital Diets for Elderly Patients With Chronic Diseases and Analysis of Factors Influencing Actual Intakes
Bibliographic record
Abstract
Background: The provision of hospital meals is considered a therapeutic intervention, and a therapeutic diet consisting of a post-discharge meal sample is provided. For elderly patients who require long-term care, it is important to determine the significance of nutrition by taking into account hospital meals, including therapeutic meals for conditions such as diabetes. Therefore, it is important to identify the factors that influence this judgment. This study aimed to investigate the difference between the expected nutritional intake via nutritional interpretation and actual nutritional intake. Methods: The study included 51 geriatric patients (77.7 ± 9.5 years; 36 males and 15 females) who could eat meals independently. The participants completed a dietary survey to determine the perceived nutritional intake obtained from hospital meal contents. Additionally, we investigated the amount of hospital meal leftovers from the medical records and the amount of nutrients from the menus to calculate the actual nutritional intake. We calculated the amount of calories, protein concentration, and non-protein/nitrogen ratio from the perceived and actual nutritional intake values. We then calculated the cosine similarity and conducted a qualitative analysis of factorial units to examine similarities between perceived and actual intake. Results: Among factors that constituted the large cosine similarity group (gender, age, etc.), gender was found as a particularly significant factor, with a high number of female patients (P = 0.014). Conclusions: Gender was found to influence the appropriate interpretation of the significance of hospital meals. The perception of such meals as samples for post-discharge dietary practice was more significant among female patients. This demonstrated that in elderly patients, it is important to consider gender differences when providing diet and convalescence guidance.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 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) | 0.001 | 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".