Code Brown: A Review of Hospital-Acquired Malnutrition and Foodborne Outbreak Management in Emergency and Surgical Wards
Bibliographic record
Abstract
Background: Within the high-stakes environment of acute care, two intersecting crises silently compromise patient safety and outcomes: hospital-acquired malnutrition (HAM) and healthcare-associated foodborne outbreaks. Patients in emergency and surgical wards are uniquely vulnerable, facing metabolic stress, increased nutritional demand, and exposure to pathogens via food and feeding practices. These dual threats are often managed in professional silos, leading to fragmented responses that fail to address their systemic interdependence. Aim: This narrative review aims to synthesize evidence on the epidemiology, etiology, and interprofessional management of HAM and foodborne illness within emergency and surgical settings. Methods: A comprehensive search of PubMed, CINAHL, Scopus, and Web of Science (2010-2024) was conducted. Results: The review identifies a high prevalence of HAM upon admission and incidence during hospitalization, exacerbated by nil-by-mouth protocols, missed meals, and poor intake monitoring. Concurrently, outbreaks of pathogens like Norovirus and Salmonella are linked to hospital food systems. Key failures include disjointed communication between dietetic and infection control teams, inadequate nursing resources for feeding assistance, and management systems that prioritize cost and efficiency over nutritional safety and infection resilience. Conclusion: HAM and foodborne outbreaks represent a "Code Brown" – a simultaneous metabolic and infectious emergency. Addressing them requires an integrated, hospital-wide strategy that repositions nutrition and food safety as inseparable components of clinical care, underpinned by interprofessional protocols, dedicated resources, and executive-level accountability.
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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 | 0.005 | 0.030 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.017 | 0.019 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".