Effect of early nutritional support on clinical course and septic complications in patients with severe burns.
Bibliographic record
Abstract
Retrospective and prospective analyses of methods for nutritional support were carried out to assess their efficiency in covering the calculated caloric and protein needs of patients with severe burns. The energy and protein needs of the patients in Group 1 (study group) with nutritional support started 24 h after the thermal trauma were determined by the Toronto formula or by means of indirect calorimetry. Nutritional support of patients in Group 2 (control group) were initiated on day 4 post-trauma. The patients in the two groups were similar in respect to age, gender, body surface area burned, and injury severity score. We found statistically significant differences in the metabolic response of both groups as measured by nitrogen balance, serum proteins, and absolute lymphocyte count. Significant differences were also registered in body weight loss, number of positive haemocultures, and the complications rate.
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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) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".