Components of Total Energy Expenditure in Healthy and Critically Ill Children: A Comprehensive Review
Bibliographic record
Abstract
BACKGROUND: Total energy expenditure (TEE) is the total energy expended by an individual to sustain life, activities, and growth. TEE is formed by four components: resting energy expenditure (REE), activity energy expenditure (AEE), growth-related energy expenditure (GEE), and the thermic effect of feeding (TEF). Some energy expenditure (EE) components may change throughout childhood and cannot be reliably estimated using prediction formulae. OBJECTIVE: To summarize measured TEE components as reported in the literature in healthy and critically ill children. METHODS: We searched MEDLINE, EMBASE, and CINAHL for studies published between 1946 and 7 September 2023. The primary outcome was energy expenditure. Included studies were published in English and measured one or more of TEE, AEE, GEE, and TEF with Indirect Calorimetry or Doubly Labeled Water in participants between 1 month and 18 years of age. We excluded studies reporting only REE or using predictive equations. Following abstraction, reported values were converted into kcal/kg/day or kcal/day as possible. Weighted mean values were calculated using median or means of EE measurements. RESULTS: We found 138 studies, 8163 patients, and 16,636 eligible measurements. The median (IQR) study sample size was 20 (12, 35) patients. TEE was the most evaluated component. The median (IQR) TEE in infants was 73.1 (67.0, 76.5), in children 78.0 (66.0, 81.3), and in adolescents was 44.2 (41.8, 51.9) kcal/kg/day. Very few studies reported on GEE and TEF. CONCLUSIONS: This is one of the first studies that summarizes components of total energy expenditure in different pediatric age groups in healthy and critically ill children. Growth- and feeding-associated energy expenditure are poorly reported in healthy children, while all components of TEE (except REE) are poorly reported in critically ill children.
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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 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.013 | 0.013 |
| 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) | 0.003 | 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".