Arvid Wretlind Lecture 2024 optimal nutrition in critically ill children: Could less be more?
Bibliographic record
Abstract
This article reflects my journey over the past several decades in challenging the long-held dogma that critically ill children should receive aggressive nutritional support-particularly high protein intake-early in the course of illness to counteract catabolism and promote anabolism. For many years, this belief dominated clinical practice, under the assumption that early and maximal nutritional delivery would improve outcomes in this vulnerable population. However, as new evidence began to emerge, this approach was called into question. The multicenter PEPaNIC (Early versus Late Parenteral Nutrition in the Pediatric Intensive Care Unit) randomized controlled trial was a pivotal step in addressing this issue. By investigating causality, the trial demonstrated that early supplementation of insufficient or contraindicated enteral nutrition with parenteral nutrition during the first week of admission to the pediatric intensive care unit (PICU) did not lead to improved clinical outcomes. In contrast, tolerating a temporary macronutrient deficit appeared to be both safe and potentially beneficial, challenging the foundational assumptions of early aggressive nutritional intervention.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.010 | 0.004 |
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".