Evaluation of Nutrition Deficits in Adult and Elderly Trauma Patients
Bibliographic record
Abstract
BACKGROUND: As metabolism is often escalated following injury, severely injured trauma patients are at risk for underfeeding and adverse outcomes. METHODS: From an international database of 12,573 critically ill, adult mechanically ventilated patients, who received a minimum of 3 days of nutrition therapy, trauma patients were identified and nutrition practices and outcomes compared with nontrauma patients. Within the trauma population, we compared nutrition practices and outcomes of younger vs older patients. RESULTS: There were 1279 (10.2%) trauma patients. They were younger, were predominantly male, had lower Acute Physiology and Chronic Health Evaluation II (APACHE II) scores, and had an overall lower body mass index compared with nontrauma patients. Eighty percent of trauma patients received enteral feeding compared with 78% of nontrauma patients. Trauma patients were prescribed more calories and protein yet received similar amounts as nontrauma patients. Nutrition adequacy was reduced in both trauma and nontrauma patients. Survival was higher in trauma patients (86.6%) compared with nontrauma patients (71.8%). When patients who died were included as never discharged, trauma patients were more rapidly discharged from the intensive care unit (ICU) and hospital. Within the trauma population, 17.5% were elderly (≥65 years). The elderly had increased days of ventilation, ICU stay, and mortality compared with younger trauma patients. In a multivariable model, age and APACHE II score, but not nutrition adequacy, were associated with time to discharge alive from the hospital. CONCLUSION: Significant nutrition deficits were noted in all patients. Elderly trauma patients have worse outcomes compared with younger patients. Further studies are necessary to evaluate whether increased nutrition intake can improve the outcomes of trauma patients, especially geriatric trauma 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 | 0.001 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".