Serum fibrosis biomarkers predict death and graft loss in liver transplantation recipients
Bibliographic record
Abstract
Noninvasive serum fibrosis biomarkers predict clinical outcomes in pretransplant patients with chronic liver disease. We investigated the role of serum fibrosis biomarkers and of changes in biomarkers in predicting death and graft loss after liver transplantation (LT). We included 547 patients who underwent LT between 1991 and 2012 and who met the following criteria: patient and graft survival > 12 months; serum fibrosis biomarkers aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis score 4 (FIB-4), and nonalcoholic fatty liver disease (NAFLD) fibrosis score available at 1 year after LT; and a minimum follow-up of 1 year. Delta of fibrosis biomarkers was defined as (end of follow-up score--baseline score)/follow-up duration. Baseline and delta fibrosis biomarkers were associated with death: APRI > 1.5 (adjusted hazard ratio [aHR], 2.2; 95% confidence interval [CI], 1.4-3.3; P < 0.001) and delta APRI > 0.5 (aHR, 5.3; 95% CI, 3.4-8.2; P < 0.001); FIB-4 > 3.3 (aHR, 1.9; 95% CI, 1.3-2.8; P = 0.002) and delta FIB-4 > 1.4 (aHR, 2.4; 95% CI, 1.4-4.1; P = 0.001); and NAFLD fibrosis score > 0.7 (aHR, 1.9; 95% CI, 1.3-2.9; P = 0.002) and delta NAFLD fibrosis score (aHR, 3.7; 95% CI, 2.6-5.4; P < 0.001). Baseline and delta fibrosis biomarkers were associated also with graft loss. In conclusion, serum fibrosis biomarkers 1 year after LT and changes in serum fibrosis biomarkers predict death and graft loss in LT recipients. They may help in risk stratification of LT recipients and identify patients requiring closer monitoring.
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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 | 0.002 |
| 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 | 0.001 | 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".