Bibliographic record
Abstract
Burn injury is the most devastating of survivable injuries and is a worldwide public health crisis. Burn injury is among the most severe metabolic stresses a patient can sustain. A major burn leads to an inflammatory response and catabolism that, when compounded by burn wound nutrient losses, can lead to severe nutrition losses and deficiencies. These losses can impair immune function and wound healing and place burn patients at high risk for organ injury and mortality. Experimental data indicate glutamine (GLN) is well positioned mechanistically, perhaps above and beyond in any other intensive care unit setting, to improve outcome in burn-injured patients. Initial clinical trial data have also shown a consistent signal of reduced mortality and reduced hospital length of stay in burn-injured subjects, without signals of clinical risk. A number of GLN clinical trials demonstrate significant reductions of gram-negative bacteremia in burn injury, perhaps via maintenance of the gut barrier or gut immune function. Current societal recommendations continue to suggest the use of GLN in burn injury. The promising clinical data in burn-injured patients, with no signals of harm, have warranted study of GLN in the definitive RE-ENERGIZE trial, which is now ongoing.
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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".