Energy requirements for ICU burn patients in whom the total body surface area affected exceeds 50 percent: a practical equation.
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Energy requirement estimations are crucial for major burn patients' nutrition management. To find a practical equation for patients with burns over >50% of their total body surface area (TBSA)in an intensive care unit (ICU). METHODS AND STUDY DESIGN: We conducted a six-week follow-up study of 21 ICU burn patients aged 17-28 years (second- and third-degree burns, TBSA: 50-90%) who were prescribed enteral nutritional support. The energy consumption ratio (ECR) was calculated by dividing the actual energy intake by the estimated energy requirement. Linear regression was used to evaluate the stability of each equation and the wound healing rate over time. RESULTS: All included patients survived. On the fifth day, among the seven equations used, the ECRs of those dependent on the basal metabolic rate and body weight, namely, 35 kcal/kg BW, BMR × 1.5, and the Toronto formula, reached 74%, 71% and 69%, respectively. The ECRs for the abovementioned formulae achieved nutritional support goal sufficiency (0.9-1.1) from the third week. Additionally, with every 1% increase in the Energy Consumption Increase Rate per week, the wound healing rate increased from 0.35% to 0.80% per week. Both the 28 and 35 kcal/kg BW formulas had the smallest regression coefficients (0.46) over 6 weeks. CONCLUSION: The 35 kcal/kg BW equation was suitable for young patients with burns over >50%TBSA in the ICU because it could be applied without equivocation, in time, and with acceptable wound healing rates. Additionally, it was well tolerated and contributed to stable management with feeding simplicity.
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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 | 0.008 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| 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".