Priorities for Nutrition Research in Pediatric Critical Care
Bibliographic record
Abstract
BACKGROUND: Widespread variation exists in pediatric critical care nutrition practices, largely because of the scarcity of evidence to guide best practice recommendations. OBJECTIVE: The objective of this paper was to develop a list of topics to be prioritized for nutrition research in pediatric critical care in the next 10 years. METHODS: A modified 3-round Delphi process was undertaken by a newly established multidisciplinary group comprising 11 international researchers in the field of pediatric critical care nutrition. Items were ranked on a 5-point Likert scale. RESULTS: Forty-five research topics (with a mean priority score >3(0-5) were identified within the following 10 domains: the pathophysiology and impact of malnutrition in critical illness; nutrition assessment: nutrition risk assessment and biomarkers; accurate assessment of energy requirements in all phases of critical illness; the role of protein intake; the role of pharmaco-nutrition; effective and safe delivery of enteral nutrition; enteral feeding intolerance: assessment and management; the role of parenteral nutrition; the impact of nutrition status and nutrition therapies on long-term patient outcomes; and nutrition therapies for specific populations. Ten top research topics (that received a mean score >4(0-5) were identified as the highest priority for research. CONCLUSIONS: This paper has identified important consensus-derived priorities for clinical research in pediatric critical care nutrition. Future studies should determine topics that are a priority for patients and parents. Research funding should target these priority areas and promote an international collaborative approach to research in this field, with a focus on improving relevant patient outcomes.
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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 | 0.115 | 0.093 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.004 | 0.003 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.008 |
| Research integrity | 0.005 | 0.006 |
| Insufficient payload (model declined to judge) | 0.004 | 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".