Which recipient pretransplant factors, such as MELD, renal function, sarcopenia, and recent sepsis influence suitability for and outcome after living donor liver transplantation? A systematic review of the literature and expert panel recommendations
Bibliographic record
Abstract
BACKGROUND: Varied access to deceased donors across the globe has resulted in differential living donor liver transplant (LDLT) practices and lack of consensus over the influence of models for end stage liver disease (MELD), renal function, sarcopenia, or recent infection on short-term outcomes. OBJECTIVES: Consider these risk factors in relation to patient selection and provide recommendations. DATA SOURCES: Ovid MEDLINE, Embase, Scopus, Google Scholar, Cochrane Central. METHODS: PRIMSA systematic review and GRADE. PROSPERO ID: RD42021260809 RESULTS: MELD >25-30 alone is not a contraindication to LDLT, and multiple studies found no increase in short term mortality in high MELD patients. Contributing factors such as muscle mass, acute physiologic assessment and chronic health evaluation score, donor age, graft weight/recipient weight ratio, and inclusion of the middle hepatic vein in a right lobe graft influence morbidity and mortality in high MELD patients. Higher mortality is observed with pretransplant renal dysfunction, but short-term mortality is rare. Sarcopenia and recent infection are not contraindications to LDLT. Morbidity and prolonged LOS are common, and more frequent in patients with renal dysfunction, nutritional deficiency or recent infection. CONCLUSIONS: When individual risk factors are studied mortality is low and graft loss is infrequent, but morbidity is common. MELD, especially with concomitant risk factors, had the greatest influence on short term outcome, and recent infection had the least. A multidisciplinary team of experts should carefully assess patients with multiple risk factors, and an optimal graft is recommended.
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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 | 0.026 | 0.106 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.013 |
| Bibliometrics | 0.010 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.007 | 0.001 |
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".