Hospital Patient’s Menu Planning Using Linear Programming in Tegucigalpa, Honduras
Bibliographic record
Abstract
Currently, the "Hospital Escuela Universitario in Tegucigalpa" faces budgetary constraints that compel it to offer a single menu for all hospital patients, regardless of their medical condition.In response to the situation, this research aimed to reduce food procurement costs while maintaining adequate nutrition by implementing a weekly menu at the hospital, utilizing operations research, specifically employing linear programming.The study was conducted with a quantitative approach, utilizing interviews and literature review as primary instruments.These instruments were crucial in understanding the hospital's current situation, and based on evidence gathered from various sources, it was decided to adopt the Mediterranean Diet as the standard for all patients.From this analysis, research variables, parameters, decision variables, and constraints were defined to formulate the mathematical model used in the study.Using the OpenSolver tool, three distinct mathematical models were generated.Each model helped identify errors and needs that required the addition of new constraints or the incorporation of additional nutritional recommendations.These decisions were supported by experts in linear programming and healthcare.It was determined that the final model is the most suitable for addressing the current needs of the hospital, as it aligns with the recommendations of the Mediterranean diet and daily nutritional requirements.The proposed menu cost is L. 389.03, deemed a viable solution since the included foods are currently available in the hospital's inventory.The mathematical model is considered flexible and adaptable, allowing for the inclusion of new constraints and data updates, thus paving the way for future projects such as the creation of customized menus for different medical conditions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".