Sarcopenia increases mortality risk in liver transplantation: a systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: Liver transplantation is an efficacious treatment option for those with liver cirrhosis. However, the prognostic role of sarcopenia in these patients is unknown. Given this background, we conducted a systematic review and meta-analysis of the impact of sarcopenia on mortality in patients listed, evaluated and undergoing liver transplantation. EVIDENCE ACQUISITION: Several databases were searched from the inception to December 2022 for observational studies regarding sarcopenia in liver transplant and mortality. We calculated the risk of mortality in sarcopenia vs. no sarcopenia using the most adjusted estimate available and summarizing the data as risk ratios (RRs) with their 95% confidence intervals (CIs). A random-effect model was considered for all analyses. EVIDENCE SYNTHESIS: Among 1135 studies initially considered, 33 articles were included for a total of 12,137 patients (mean age: 55.3 years; 39.4% females). Over a median of 2.6 years and after adjusting for a median of 3 covariates, sarcopenia increased the risk of mortality approximately 2-fold (RR: 2.01; 95% CI: 1.70-2.36). After accounting for publication bias, the re-calculated RR was 1.75 (95% CI: 1.49-2.06). The quality of the studies was generally low, as determined by the Newcastle Ottawa Scale. CONCLUSIONS: Sarcopenia was significantly linked with an increased risk of mortality in patients listed, evaluated, and undergoing a liver transplantation, indicating the need of interventional studies in this special population with the main aim to reverse this potential reversible condition and decrease mortality risk.
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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 | 0.011 | 0.028 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.033 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".