Serial Liver Stiffness Measurements and Monitoring of Liver-Transplanted Patients in a Real-Life Clinical Practice
Bibliographic record
Abstract
BACKGROUND: Liver transplanted patients need close surveillance for early signs of graft disease. OBJECTIVES: Transient elastography can safely be repeated over time, offering serial liver stiffness measurement values. Serial stiffness measurements were compared to single baseline stiffness measurements in predicting the appearance of liver-related clinical events and guiding subsequent clinical decisions. METHODS: One hundred and sixty liver transplanted patients were observed for three years in our real-life practice. RESULTS: Liver stiffness measurements were stable in 75% of patients, decreased in 4% of patients, and increased in 21% of patients. The pattern of increased stiffness measurements was associated with both HCV-RNA positive status and the presence of an active biliary complication of liver transplantation and was more predictive of a clinically significant event resulting from any disease of the transplanted liver when compared to a stable pattern or to a single liver stiffness measurement. The procedures that were consequently performed were often diagnostic for unexpected situations, both in HCV-RNA positive and HCV-RNA negative patients. CONCLUSIONS: The pattern of longitudinally increased liver stiffness measurements efficiently supported clinical decisions for individualized management strategies. Repeated transient elastography in real-life clinical practice appears to have a practical role in monitoring liver transplanted patients.
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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 | 0.001 | 0.008 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 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".