Can V<scp>co</scp><sub>2</sub>‐Based Estimates of Resting Energy Expenditure Replace the Need for Indirect Calorimetry in Critically Ill Children?
Bibliographic record
Abstract
Background: Optimal energy provision, guided by measured resting energy expenditure (REE), is fundamental in the care of critically ill children. REE should be determined by indirect calorimetry (IC), which has limited availability. Recently, a novel equation was developed for estimating REE derived from carbon dioxide production (Vco2). The aim of this study was to validate the accuracy of this equation in a population of critically ill children following cardiopulmonary bypass (CPB). Methods: This is an ancillary study to a larger trial of children undergoing CPB. Respiratory mass spectrometry was used measure oxygen consumption (Vo2) and Vco2. REE was then calculated according to the established Weir equation (REEW) and the modified, Vco2‐based equation (REECO2). The agreement between the 2 measurements was assessed using Bland‐Altman plots and mixed‐model regressions accounting for repeated measures. Results: Data from 104 patients, which included 575 paired measurements, were included. The agreement between REEW and REECO2 was biased during the 72‐hour observation period post CPB, with a mean percentage error between measurements of 11% (±7%). The most important determinant of the bias with the Vco2‐based equation was the respiratory quotient (RQ). The percentage error between REEW and REECO2 dropped to 4.4% (±2.4%) in those with an RQ between 0.8 and 1. The within‐subject variability for RQ in this cohort was wide (11%). Conclusions: IC remains the most accurate method to determine the REE of critically ill patients. Widespread availability of Vco2 data renders Vco2‐based approaches to measurement of REE attractive; however, further research is needed to ensure that REE is estimated accurately.
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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 | 0.013 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 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 | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".