Nutrition support practices in critically ill head-injured patients: a global perspective
Bibliographic record
Abstract
BACKGROUND: Critical illness following head injury is associated with a hypermetabolic state but there are insufficient epidemiological data describing acute nutrition delivery to this group of patients. Furthermore, there is little information describing relationships between nutrition and clinical outcomes in this population. METHODS: We undertook an analysis of observational data, collected prospectively as part of International Nutrition Surveys 2007-2013, and extracted data obtained from critically ill patients with head trauma. Our objective was to describe global nutrition support practices in the first 12 days of hospital admission after head trauma, and to explore relationships between energy and protein intake and clinical outcomes. Data are presented as mean (SD), median (IQR), or percentages. RESULTS: Data for 1045 patients from 341 ICUs were analyzed. The age of patients was 44.5 (19.7) years, 78% were male, and median ICU length of stay was 13.1 (IQR 7.9-21.6) days. Most patients (94%) were enterally fed but received only 58% of estimated energy and 53% of estimated protein requirements. Patients from an ICU with a feeding protocol had greater energy and protein intakes (p <0.001, 0.002 respectively) and were more likely to survive (OR 0.65; 95% CI 0.42-0.99; p = 0.043) than those without. Energy or protein intakes were not associated with mortality. However, a greater energy and protein deficit was associated with longer times until discharge alive from both ICU and hospital (all p <0.001). CONCLUSION: Nutritional deficits are commonplace in critically ill head-injured patients and these deficits are associated with a delay to discharge alive.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 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".