An Exploratory Retrospective Study of Factors Affecting Energy Expenditure in Critically Ill Children
Bibliographic record
Abstract
BACKGROUND: Accurate measurement of energy expenditure is not widely available. Patient and clinical factors associated with energy expenditure have been poorly explored, leading to errors in estimation formulae. The objective of this study was to determine clinical factors associated with measured energy expenditure (MEE), expressed in kcal/kg/d, in critically ill children. METHODS: This was a retrospective study at 2 Canadian pediatric intensive care units (ICUs). Patients were mechanically ventilated children who had 1 or more MEE using indirect calorimetry. Associations between MEE and 28 clinical factors were evaluated in univariate regression and 16 factors in a multivariate regression model accounting for repeated measurements. RESULTS: Data from 239 patients (279 measurements) were analyzed. Median (Q1, Q3) MEE was 34.8 (26.8, 46.2) kcal/kg/d. MEE was significantly associated with weight, heart rate, diastolic blood pressure, ICU day of indirect calorimetry (P = 0.004), minute ventilation, vasoactive inotropic score (P = 0.004), opioids, chloral hydrate, dexmedetomidine, inhaled salbutamol (P = 0.02), and propofol dose (all P < 0.0001 unless otherwise specified) in the final multivariate regression model. CONCLUSIONS: This study demonstrated association between MEE (kcal/kg/d) and factors not previously explored in pediatric critical illness. Further evaluation of these factors to confirm associations and more precisely quantify the magnitude of effect is required to support refinement of formulae to estimate energy expenditure.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".