Fat‐free muscle mass in magnetic resonance imaging predicts acute‐on‐chronic liver failure and survival in decompensated cirrhosis
Bibliographic record
Abstract
Muscle mass seems to be a prognostic marker in patients with liver cirrhosis. However, reported methods to quantify muscle mass are heterogeneous, consented cutoff values are missing, and most studies have used computed tomography. This study evaluated fat-free muscle area (FFMA) as a marker of sarcopenia using magnetic resonance imaging (MRI) in patients with decompensated cirrhosis with transjugular intrahepatic portosystemic shunt (TIPS). The total erector spinae muscle area and the intramuscular fat tissue area were measured and subtracted to calculate the FFMA in 116 patients with cirrhosis by TIPS and MRI. The training cohort of 71 patients compared computed tomography-measured transversal psoas muscle thickness with FFMA. In 15 patients MRI was performed before and after TIPS, and in 12 patients follistatin serum measurements were carried out. The results on FFMA were confirmed in a validation cohort of 45 patients. FFMA correlated with follistatin and transversal psoas muscle thickness and showed slightly better association with survival than transversal psoas muscle thickness. Gender-specific cutoff values for FFMA were determined for sarcopenia. Decompensation (ascites, overt hepatic encephalopathy) persisted after TIPS in the sarcopenia group but resolved in the nonsarcopenia group. Sarcopenic patients showed no clinical improvement after TIPS as well as higher mortality, mainly due to development of acute-on-chronic liver failure. FFMA was an independent predictor of survival in these patients. CONCLUSION: This study offers an easy-to-apply MRI-based measurement of fat-free muscle mass as a marker of sarcopenia in decompensated patients; while TIPS might improve sarcopenia and thereby survival, persistence of sarcopenia after TIPS is associated with a reduced response to TIPS and a higher risk of acute-on-chronic liver failure development and mortality. (Hepatology 2018;67:1014-1026).
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| 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 | 0.000 |
| 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".