Systematic review of factors associated with energy expenditure in the critically ill
Bibliographic record
Abstract
BACKGROUND AND AIMS: Indirect calorimetry is the reference standard for energy expenditure measurement. Predictive formulae that replace it are inaccurate. Our aim was to review the patient and clinical factors associated with energy expenditure in critically ill patients. METHODS: We conducted a systematic review of the literature. Eligible studies were those reporting an evaluation of factors and energy expenditure. Energy expenditure and factor associations with p-values were extracted from each study, and each factor was classified as either significantly, indeterminantly, or not associated with energy expenditure. Regression coefficients were summarized as measures of central tendency and spread. Metanalysis was performed on correlations. RESULTS: The search strategy yielded 8521 unique articles, 307 underwent full text review, and 103 articles were included. Most studies were in adults. There were 95 factors with 352 evaluations. Minute volume, weight, age, % body surface area burn, sedation, post burn day, and caloric intake were significantly associated with energy expenditure. Heart rate, fraction of inspired oxygen, respiratory rate, respiratory disease diagnosis, positive end expiratory pressure, intensive care unit days, C- reactive protein, and size were not associated with energy expenditure. Multiple factors (n = 37) were identified with an unclear relationship with energy expenditure and require further evaluation. CONCLUSIONS: An important interval step in the development of accurate formulae for energy expenditure estimation is a better understanding of relationships between patient and clinical factors and energy expenditure. The review highlights the limitations of currently available data, and identifies important factors that are not included in current prediction formulae of the critically ill.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.003 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.009 |
| Bibliometrics | 0.007 | 0.007 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".