Hypermetabolism following Moderate to Severe Traumatic Acute Brain Injury: A Systematic Review
Bibliographic record
Abstract
Elevations of metabolic rate following traumatic brain injury (TBI) have been reported previously, with estimates ranging from 32% to 200% above normal values. The aim of this systematic review was to describe the pattern of energy expenditure during the first 30 days following TBI. We searched six databases for trials that measured the energy expenditure at least once during the first 30 days post-injury and compared that value to one that would be predicted in the non-injured state. We identified 24 studies, three of which were randomized controlled trials (RCTs). The sample sizes of the included studies ranged from 6 to 80 (mean, 24.7), and the mean Glasgow Coma Scale (GCS) score of subjects was 4.8. Mean energy expenditure, expressed as a percentage of a predicted value, ranged from 75% to 200%. The lowest values were reported in patients admitted in brain death. Several factors were found to have modulating effects on energy expenditure. The administration of paralyzing agents, sedatives, or barbiturates reduced metabolic rate by approximately 12-32%. Propranolol and morphine were associated with smaller decreases in energy expenditure. Factors that do not appear to augment the hypermetabolic response included the administration of steroids and method of feeding (enteral vs. parenteral). Based on our results, it was unclear if elevated temperature, the presence of extracranial injury, or the severity of injury further exacerbate hypermetabolism. We conclude that energy expenditure following TBI is highly variable, and the use of standard factors to estimate the energy needs of individual patients are inappropriate and should be discouraged.
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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 | 0.004 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.006 |
| Bibliometrics | 0.008 | 0.010 |
| 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".