718 Indirect Calorimetry is Necessary to Optimize Nutrition in Large Burns
Bibliographic record
Abstract
Abstract Introduction Assessing nutritional requirements in large total body surface area (TBSA) burns is a challenge due to frequent metabolic variation. Studies have compared indirect calorimetry (IC) with predictive equations and identified formulas that are frequently used in the absence of IC. Large TBSA burns remain poorly understood, as does the role of changing metabolic rate and multiple surgical procedures over the course of recovery. We sought to understand whether these equations remain reliable in these situations. Methods The patient is a 31 year old, 92% full-thickness TBSA burn who was studied over the first 75 days of hospitalization at various points per institutional IC guidelines. Using total energy expenditure (TEE) by IC as the standard, we assessed variation of each predictive equation for accuracy. Only the Milner and Toronto formulas are dynamic, and we analyzed variation by post-burn day (PBD) compared to dates of major surgical procedures in these studies. The patient was excised on PBD 1, 2, 5, 7, 9, 21, and 25; he was grafted on POD 5, 11, 20, 41, and 56. Weekly monitoring of prealbumin and CRP indicated adequate nutrition. Results On post-burn day (PBD) 5, when all major burns were excised, all predictive equations inappropriately estimated the patient’s energy expenditure. On longitudinal analysis of 16 IC studies (Table 2), the Milner equation was most accurate, estimating within 5% variance of TEE at 5 time points (31%). The Toronto formula did not estimate within 5% variance at any time point. No energy equation consistently and accurately estimated energy expenditure over all calculated time points. Conclusions Although predictive equations are frequently used, in high TBSA burns with many operations and changing nutritional needs, equations are not as accurate as IC. Given that over- or under-estimating needs result in many complications avoided with IC, we propose frequent IC for intubated, high TBSA patients.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".