The role of preoperative optimization of the nutritional status on the improvement of short‐term outcomes after liver transplantation? A review of the literature and expert panel recommendations
Bibliographic record
Abstract
BACKGROUND: Malnutrition is a known risk factor for postoperative morbidity and mortality in patients awaiting liver transplantation (LT). Malnutrition is a potentially reversible risk factor, though there are no clear guidelines on the best mechanism for an improvement. It also remains unclear if preoperative nutritional interventions have benefits to post-transplant outcomes for transplant recipients. OBJECTIVES: Primary objective: To identify if preoperative optimization of nutritional status is associated with improved short-term outcomes after LT. SECONDARY OBJECTIVES: To determine if preoperative improvement of malnutrition improves short-term outcomes after LT, as well as if weight loss in obese patients affects short-term outcomes after LT. DATA SOURCES: Ovid MEDLINE, Embase, Scopus, Google Scholar, and Cochrane Central. METHODS: Systematic review following PRISMA guidelines and recommendations using the GRADE approach derived from an international expert panel. POSPERO Protocol ID: CRD42021237450 RESULTS: 3851 records were identified in searching the databases, 3843 records were excluded by not fulfilling eligibility criteria. Seven full-text articles were included for the final analysis of which three were randomized controlled trials, one was prospective observational studies, and three were retrospective observational studies. No appreciable difference in mortality, post-transplant complication rate was noted across the studies. Length of stay (LOS) was noted to be shorter in two observational studies of Vitamin D deficiency in liver transplant patients. CONCLUSIONS: We have made a weak recommendation supporting pre-transplant nutritional supplementation due to possible benefit in reducing LOS as well as the lack of harm (Quality of Evidence low | Grade of Recommendation; Weak). No effective conclusions were reached for the secondary objectives due to the conflicting evidence.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| 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 | 0.001 |
| 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".