Nutritional aspects of prehabilitation in adults with cirrhosis awaiting liver transplant
Bibliographic record
Abstract
Malnutrition, sarcopenia (low muscle mass), and physical frailty have gained increasing recognition in candidates for liver transplant (LT) as these conditions can impact postoperative functional capacity. Multidimensional prehabilitation programs have been proposed as a safe intervention in adults awaiting LT but the nutritional pillar of prehabilitation has been understudied. This review summarizes the nutritional recommendations for prehabilitation for individuals with cirrhosis awaiting LT. Three major aspects of nutritional prehabilitation are discussed: (1) Assess: Evaluate nutritional status and assess for malnutrition, sarcopenia, and frailty to guide the nutritional prehabilitation intervention intensity, increasing across universal, targeted, and specialist levels; (2) Intervene: Prescribe a nutritional prehabilitation intervention to meet established nutrition guidelines in cirrhosis with a targeted focus on improving nutritional status and muscle health; (3) Reassess: Follow-up based on the required intensity of nutritional care with as needed intervention adjustment. Topics covered in the review include nutritional care levels for prehabilitation, energy prescriptions across body mass index strata, detailed considerations around protein intake (amount, distribution, and quality), carbohydrate and fat intake, other nutritional considerations, and the potential role of dietary supplements and nutraceuticals. Future research is warranted to more accurately evaluate energy needs, evaluate emerging dietary supplementation strategies, and establish the role of nutraceuticals alongside food-based interventions. While the general principles of nutritional prehabilitation are ready for immediate application, future large-scale randomized controlled trials in this space will help to quantify the benefit that can be gained by transitioning the LT approach from passive "transplant waitlist time" to active "transplant preparation time."
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 | 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".