Measured resting energy expenditure and predicted resting energy expenditure based on ASPEN critical care guidelines for nutrition support: An agreement study
Bibliographic record
Abstract
BACKGROUND: Predictive equations often inaccurately estimate energy needs in critically ill patients. This study evaluated the level of agreement between resting energy expenditure using 12 and 25 kcal/kg as recommended by the 2021 American Society for Parenteral and Enteral Nutrition critical care guidelines for nutrition support and energy expenditure measured by indirect calorimetry in patients in the intensive care unit. METHODS: An agreement study was conducted on mechanically ventilated adults who had a documented measured energy expenditure within 10 days of intensive care unit admission. Agreement was assessed using Bland-Altman plots and Wilcoxon signed rank tests. A subgroup analysis was performed for patients with a body mass index of ≥30 kg/m² using actual body weight, adjusted body weight, and ideal body weight. Correlations between measured energy expenditure and patient characteristics were also explored. RESULTS: . The 12 kcal/kg and 25 kcal/kg differed significantly from measured energy expenditure (P < 0.001). Bland-Altman plots showed mean biases of -644.6 kcal/day for 12 kcal/kg and 406.5 kcal/day for 25 kcal/kg. In the body mass index ≥30 kg/m² subgroup (n = 22), 12 kcal/kg underestimated measured energy expenditure across all weights, and 25 kcal/kg was more accurate when using ideal or adjusted body weights. CONCLUSIONS: Predicted energy expenditure using 12 kcal/kg and 25 kcal/kg based on the 2021 American Society for Parenteral and Enteral Nutrition critical care guidelines for nutrition support had poor agreement with measured energy expenditure in mechanically ventilated 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 | 0.017 | 0.051 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.001 |
| 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".