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Record W4412459833 · doi:10.1016/j.clnu.2025.07.009

Arvid Wretlind Lecture 2024 optimal nutrition in critically ill children: Could less be more?

2025· review· en· W4412459833 on OpenAlexfundno aff
Koen F. M. Joosten

Bibliographic record

VenueClinical Nutrition · 2025
Typereview
Languageen
FieldNursing
TopicClinical Nutrition and Gastroenterology
Canadian institutionsnot available
FundersUniversitaire Ziekenhuizen Leuven, KU LeuvenUniversitair Ziekenhuis BrusselHospital for Sick ChildrenErasmus Medisch CentrumUniversity of Glasgow
KeywordsMedicineParenteral nutritionCritically illIntensive care medicineRandomized controlled trialCausality (physics)Intensive care unitIntervention (counseling)Critical illnessIntensive carePopulationClinical trialPediatricsSurgeryNursingInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.006
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow), Research integrity
Consensus categoriesResearch integrity
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.606
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.006
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0050.003
Bibliometrics0.0010.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0010.000
Research integrity0.0040.005
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.068
GPT teacher head0.432
Teacher spread0.364 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; both teacher heads agree on what is shown here.

Study designNot applicable
Domainnot available
GenreReview

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".

Quick stats

Citations1
Published2025
Admission routes1
Has abstractyes

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