Geometric framework reveals that a moderate protein, high carbohydrate intake is optimal for severe burn injury in mice
Bibliographic record
Abstract
Nutritional therapy is a cornerstone of burns management. The optimal macronutrient intake for wound healing after burn injury has not been identified, although high-energy, high-protein diets are favoured. The present study aimed to identify the optimal macronutrient intake for burn wound healing. The geometric framework (GF) was used to analyse wound healing after a 10 % total body surface area contact burn in mice ad libitum fed one of the eleven high-energy diets, varying in macronutrient composition with protein (P5-60 %), carbohydrate (C20-75 %) and fat (F20-75 %). In the GF study, the optimal ratio for wound healing was identified as a moderate-protein, high-carbohydrate diet with a protein:carbohydrate:fat (P:C:F) ratio of 1:4:2. High carbohydrate intake was associated with lower mortality, improved body weight and a beneficial pattern of body fat reserves. Protein intake was essential to prevent weight loss and mortality, but a protein intake target of about 7 kJ/d (about 15 % of energy intake) was identified, above which no further benefit was gained. High protein intake was associated with delayed wound healing and increased liver and spleen weight. As the GF study demonstrated that an initial very high protein intake prevented mortality, a very high-protein, moderate-carbohydrate diet (P40:C42:F18) was specifically designed. The dynamic diet study was also designed to combine and validate the benefits of an initial very high protein intake for mortality, and subsequent moderate protein, high carbohydrate intake for optimal wound healing. The dynamic feeding experiment showed switching from an initial very high-protein diet to the optimal moderate-protein, high-carbohydrate diet accelerated wound healing whilst preventing mortality and liver enlargement.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".