Performance of transient elastography and serum fibrosis biomarkers for non-invasive evaluation of recurrent fibrosis after liver transplantation: A meta-analysis
Bibliographic record
Abstract
Recurrent fibrosis after liver transplantation (LT) impacts on long-term graft and patient survival. We performed a meta-analysis to compare the accuracy of non-invasive methods to diagnose significant recurrent fibrosis (stage F2-F4) following LT. Studies comparing serum fibrosis biomarkers, namely AST-to-platelet ratio index (APRI), fibrosis score 4 (FIB-4), or transient elastography (TE) with liver biopsy in LT recipients were systematically identified through electronic databases. In the meta-analysis, we calculated the weighted pooled odds ratio and used a fixed effect model, as there was no significant heterogeneity between studies. Eight studies were included for APRI, four for FIB-4, and twelve for TE. The mean prevalence of significant liver fibrosis was 37.4%. The summary odds ratio was significantly higher for TE (21.17, 95% CI confidence interval 14.10-31.77, p = 1X10-30) as compared to APRI (9.02, 95% CI 5.79-14.07; p = 1X10-30) and FIB-4 (7.08, 95% CI 4.00-12.55; p = 1.93X10-11). In conclusion, TE performs best to diagnose recurrent fibrosis in LT recipients. APRI and FIB-4 can be used as an estimate of significant fibrosis at centres where TE is not available. Longitudinal assessment of fibrosis by means of these non-invasive tests may reduce the need for liver biopsy.
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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 | 0.016 | 0.029 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.014 | 0.060 |
| Bibliometrics | 0.004 | 0.004 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".