Illness Severity by MELD-Na is not Associated with Energy Expenditure in Cirrhotic Liver Transplant Candidates
Bibliographic record
Abstract
Purpose: Hypermetabolism contributes to malnutrition incirrhosis and it often goes undetected. Indirect calorimetry is a high-cost, time-consuming tool which is not accessible to many health care institutions. Predictive equations often underestimate true caloric requirements in cirrhosis. The aim of this study was to assess whether illness severity, defined by MELD-Na score, is associated with increases in measured Resting Energy Expenditure (REE) in patients awaiting liver transplantation. We also sought to quantify a stress factor for improved accuracy of predicted energy requirements in this population. Methods: Data from 40 patients awaiting liver transplantation were retrospectively assessed. MELD-Na was calculated from serum markers; measured REE was quantified using a indirect calorimetry; and predicted REE was determined by the Harris-Benedict equation. Results: MELD-Na scores were not associated with measured REE or prevalence of Hypermetabolism. Forty-three percent of patients required a 20-50% addition to predictive estimates to cover their basal metabolic needs. Conclusions: Our findings suggest a stress factor of 1.2 to 1.3 should be considered when predicting caloric requirements of candidates awaiting liver transplantation in the absence of indirect calorimetry. Illness severity by MELD-Na does not appear to be a useful marker in identifying patients who are hypermetabolic and at higher risk of underfeeding. Keywords: End-stage liver disease; Resting energy expenditure; Indirect calorimetry; Nutritional needs; Liver transplantation;
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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 | 0.000 | 0.002 |
| 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) | 0.002 | 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".