Predictive Equations Are Inaccurate in the Estimation of the Resting Energy Expenditure of Children With End‐Stage Liver Disease
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Malnutrition is common in children with end-stage liver disease (ESLD) and is associated with increased morbidity and mortality. The inability to accurately estimate energy needs of these patients may contribute to their poor nutrition status. In clinical practice, predictive equations are used to calculate resting energy expenditure (cREE). The objective of this study is to assess the accuracy of commonly used equations in pediatric patients with ESLD. METHODS: Retrospective study performed at the Hospital for Sick Children. Clinical, laboratory, and indirect calorimetry data from children listed for liver transplant between February 2013 and December 2014 were reviewed. Calorimetry results were compared with cREE estimated using the Food and Agriculture Organization/World Health Organization/United Nations University (FAO/WHO/UNU), Schofield [weight], and Schofield [weight and height] equations. RESULTS: Forty-five patients were included in this study. The median age was 9 months, and the most common indication for transplantation was biliary atresia (64%). The Schofield [weight and height], FAO/WHO/UNU, and Schofield [weight] equations were compared with indirect calorimetry and found to have a mean (SD) difference of 48.8 (344.0), 59.3 (229.8), and 206.5 (502.6) kcal/d, respectively. The FAO/WHO/UNU, Schofield [weight], and Schofield [weight and height] equations introduced a mean error of 21%, 38%, and 76%, respectively. The FAO/WHO/UNU equation tended to underestimate, whereas the Schofield equations overestimated the REE. CONCLUSIONS: Commonly used predictive equations perform poorly in infants and young children with ESLD. Indirect calorimetry should be used when available to guide energy provision, particularly in children who are already malnourished.
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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 | 0.005 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
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.
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".