Optimizing Nutrition in Pediatric Heart Failure: The Crisis Is Over and Now It’s Time to Feed
Bibliographic record
Abstract
Pediatric heart failure is a complex disease occurring when cardiac output is unable to meet the metabolic demands of the body. With improved surgical interventions and medical therapies, survival rates have improved, and care has shifted from focusing on survival to optimizing quality of life and health outcomes. Based on current literature, this review addresses the nutrition needs of infants and children in heart failure and describes the pathophysiology and metabolic implications of this disease. The prevalence of wasting in pediatric heart failure has been reported to be as high as 86%, highlighting the importance of nutrition assessment through all stages of treatment to provide appropriate intake of energy, protein, and micronutrients. The etiology of malnutrition in pediatric heart failure is multifactorial and involves hypermetabolism, decreased intake, increased nutrient losses, inefficient utilization of nutrients, and malabsorption. Children in heart failure often present with tachypnea, tachycardia, fatigue, nausea, and vomiting and consequently may not be able to meet their nutrition requirements through oral intake alone. Nutrition support, including enteral nutrition and parenteral nutrition, should be considered an essential part of routine care. The involvement of multiple allied health professionals may be needed to create a feeding therapy plan to support patients and their families. With appropriate nutrition interventions, clinical outcomes and quality of life can be significantly improved.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".