Critical Role of Nutrition in Improving Quality of Care: An Interdisciplinary Call to Action to Address Adult Hospital Malnutrition
Bibliographic record
Abstract
The current era of health care delivery, with its focus on providing high-quality, affordable care, presents many challenges to hospital-based health professionals. The prevention and treatment of hospital malnutrition offers a tremendous opportunity to optimize the overall quality of patient care, improve clinical outcomes, and reduce costs. Unfortunately, malnutrition continues to go unrecognized and untreated in many hospitalized patients. This article represents a call to action from the interdisciplinary Alliance to Advance Patient Nutrition to highlight the critical role of nutrition intervention in clinical care and to suggest practical ways to promptly diagnose and treat malnourished patients and those at risk for malnutrition. We underscore the importance of an interdisciplinary approach to addressing malnutrition both in the hospital and in the acute post-hospital phase. It is well recognized that malnutrition is associated with adverse clinical outcomes. Although data vary across studies, available evidence shows that early nutrition intervention can reduce complication rates, length of hospital stay, readmission rates, mortality, and cost of care. The key is to systematically identify patients who are malnourished or at risk and to promptly intervene. We present a novel care model to drive improvement, emphasizing the following six principles: (1) create an institutional culture where all stakeholders value nutrition; (2) redefine clinicians' roles to include nutrition care; (3) recognize and diagnose all malnourished patients and those at risk; (4) rapidly implement comprehensive nutrition interventions and continued monitoring; (5) communicate nutrition care plans; and (6) develop a comprehensive discharge nutrition care and education plan.
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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 | 0.050 | 0.069 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.008 | 0.018 |
| Scholarly communication | 0.024 | 0.021 |
| Open science | 0.004 | 0.027 |
| Research integrity | 0.020 | 0.042 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".