Sarcopenia As a Predictor of Survival and Complications of Patients With Cirrhosis After Liver Transplantation: A Systematic Review and Meta‐Analysis
Bibliographic record
Abstract
INTRODUCTION: This systematic review/meta-analysis evaluated the impact of sarcopenia in patients with cirrhosis before liver transplantation (LT) on outcomes after LT. METHODS: A systematic search was conducted in six medical databases until February 2022. The primary outcome was overall mortality after LT, while several secondary outcomes including liver graft survival and rejection, the need for transfusions, the length of the intensive care unit (ICU) and hospital stay, and surgical complications were evaluated. Sub-group analyses and meta-regression analyses were also performed. RESULTS: Fifty-three studies were evaluated in the systematic review, of which 30, including 5875 patients, were included in the meta-analysis. All studies included were cohort studies of good/high quality on the Newcastle-Ottawa scale (NOS), while in our analysis no publication bias was found, although there was substantial heterogeneity between the studies. Muscle mass was assessed using skeletal muscle index (SMI) in 14 studies, psoas muscle area (PMA) in seven studies, and psoas muscle index (PMI) in four studies. The prevalence of pre-LT sarcopenia ranged from 14.7% to 88.3%. Pre-LT sarcopenia was significantly associated with post-LT mortality (Relative Risk [RR] = 1.84, 95% CI:1.41,2.39), as well as with a high risk of infections post-LT, surgical complications, fresh frozen plasma (FFP) transfusions, and ICU length of stay (LOS). CONCLUSIONS: Pre-LT sarcopenia in patients with cirrhosis is a strong risk factor for clinically meaningful adverse outcomes after LT. Assessment may help identify patients at the highest risk for poor outcomes who may benefit from targeted interventions.
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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 | 0.009 | 0.023 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.029 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| 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".