Nutrition after severe burn injury
Bibliographic record
Abstract
PURPOSE OF REVIEW: Severe burn injury causes significant metabolic changes and demands that make nutritional support particularly important. Feeding the severe burn patient is a real challenge in regard to the specific needs and the clinical constraints. This review aims to challenge the existing recommendations in the light of the few recently published data on nutritional support in burn patients. RECENT FINDINGS: Some key macro- and micro-nutrients have been recently studied in severe burn patients. Repletion, complementation or supplementation of omega-3 fatty acids, vitamin C, vitamin D, antioxidant micronutrients may be promising from a physiologic perspective, but evidence of benefits on hard outcomes is still weak due to the studies' design. On the contrary, the anticipated positive effects of glutamine on the time to discharge, mortality and bacteremias have been disproved in the largest randomized controlled trial investigating glutamine supplementation in burns. An individualized approach in term of nutrients quantity and quality may proof highly valuable and needs to be validated in adequate trials. The combination of nutrition and physical exercises is another studied strategy that could improve muscle outcomes. SUMMARY: Due to the low number of clinical trials focused on severe burn injury, most often including limited number of patients, developing new evidence-based guidelines is challenging. More high-quality trials are needed to improve the existing recommendations in the very next future.
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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.002 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.010 | 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".