Incidence and Predictors of Advanced Liver Fibrosis by a Validated Serum Biomarker in Liver Transplant Recipients
Bibliographic record
Abstract
Background and Aims. Serum fibrosis biomarkers have shown good accuracy in the liver transplant (LT) population. We employed a simple serum biomarker to elucidate incidence and predictors of advanced fibrosis after LT over a long follow-up period.Methods. We included 440 consecutive patients who underwent LT between 1991 and 2013. Advanced liver fibrosis was defined as FIB-4 > 3.25 beyond 12 months after LT.Results. Over 2030.5 person-years (PY) of follow-up, 189 (43%) developed FIB-4 > 3.25, accounting for an incidence of 9.3/100 PY (95% confidence interval [CI], 8.1–10.7). Advanced fibrosis was predicted by chronic HCV infection (adjusted hazard ratio (aHR) = 3.96, 95% CI 2.92–5.36, p < 0.001), hypoalbuminemia (aHR = 2.31, 95% CI 1.72–3.09; p < 0.001), and hyponatremia (aHR = 1.48, 95% CI 1.09–2.01; p = 0.01). LT recipients with more than 1 predictor had a higher incidence of advanced fibrosis, the highest being when all 3 predictors coexisted (log-rank: p < 0.001).Conclusions. Chronic HCV infection, hypoalbuminemia, and hyponatremia predict progression to advanced liver fibrosis following LT. Patients with these risk factors should be serially monitored using noninvasive fibrosis biomarkers and prioritized for 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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".