Energy requirements and the use of predictive equations versus indirect calorimetry in critically ill patients
Bibliographic record
Abstract
Nutrition support has been shown to have a positive impact on critically ill patients who meet their defined goals of nutrition therapy. However, inappropriate energy assessment can contribute to under- or overfeeding resulting in deleterious effects. Thus, assessment of energy expenditure in critically ill patients is crucial to prevent negative impacts from inappropriate feeding. Currently, the optimal energy requirement and appropriate energy assessment in these patients is controversial. Indirect calorimetry or predictive equations have been suggested to evaluate energy expenditure in critically ill patients. Indirect calorimetry is a gold standard for evaluating energy expenditure, but it is not always available and has some limitations. Many predictive equations, therefore, have been developed to predict energy expenditure in critically ill patients. However, these equations cannot be used generally in these patients since they were developed in a unique patient population. Many studies compared measured energy expenditure with predictive energy expenditure, but the data regarding accuracy is not robust. Therefore, clinicians should consider using these equations carefully based on the current supporting data. Indirect calorimetry is recommended for use in evaluating energy expenditure in critically ill patients if it is available.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".