Measuring Energy Requirements of Traumatic Brain Injury Patients in Pediatric Intensive Care With Indirect Calorimetry: A Comparison With Empiric Methods*
Bibliographic record
Abstract
OBJECTIVES: Energy requirements following moderate or severe pediatric traumatic brain injury (TBI) have not been fully elucidated. Indirect calorimetry (IC) is the gold standard for measuring resting energy expenditure (MREE) in PICU. However, technical complexity limits its use. We aimed to determine whether MREE differs from standard of care energy estimation and delivery in a cohort of pediatric patients following moderate to severe TBI during PICU admission. DESIGN: Retrospective case series study. SETTING: Single-center, 16-bed general PICU in Canada between May 2011 and January 2019. PATIENTS: Children (0-18 yr) admitted to a PICU for moderate (Glasgow Coma Scale [GCS] 9-12) to severe TBI (GCS < 9) and had an IC study performed while mechanically ventilated. INTERVENTIONS: None. MEASUREMENTS AND MAIN RESULTS: During the study period, 245 patients were admitted with the diagnosis of trauma with TBI. The study includes a convenience sample of 26 patients with severe ( n = 23) and moderate ( n = 3) TBI who underwent a total of 34 IC measurements. MREE varied considerably from 29% to 144% of predicted energy expenditure. Using Bland-Altman comparative analysis, neither Schofield nor World Health Organization predictive equations were in agreement with MREE. Only one measurement revealed that the patient was appropriately fed (energy provided in nutrition support was within 10% of MREE); 10 (38%) measurements revealed overfeeding and 15 (58%) underfeeding at the time of testing. CONCLUSIONS: The present study adds to the small body of literature highlighting the limitations of predictive equations to evaluate energy requirements following moderate to severe pediatric TBI. IC, when feasible, should be used as the preferred method to orient PICU teams to feed such vulnerable patients.
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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 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 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".