PSC-specific prognostic scores associated with graft loss and overall mortality in recurrent PSC after liver transplantation
Bibliographic record
Abstract
BACKGROUND: Primary sclerosing cholangitis (PSC) is a progressive liver disease with no treatment apart from liver transplantation (LT). After LT, patients can develop recurrent PSC (rPSC). The United-Kingdom (UK-PSC) and Amsterdam-Oxford (AOPSC) scores are used as prognostic models for PSC outcomes. AIM: We aimed to assess these scores as predictive tools for graft loss and overall mortality in rPSC. METHODS: We evaluated 67 people who developed rPSC. Using Cox regression models, we quantified associations between UK-PSC and AOPSC scores and graft loss and overall mortality. Cut-offs were established using receiver operator characteristic analysis and the highest Youden index. RESULTS: Fifty-one individuals (76.1%) were males, with a mean age of 40±15 years. Both UK-PSC and AOPSC scores were independently associated with graft loss (hazard ratio [HR] 2.43 (p < 0.001) and HR 3.45 (p < 0.001), respectively), but only the UK-PSC score was independently associated with overall mortality (HR 2.63 (p = 0.009)). Individuals with UK-PSC ≥-4.2 (6.1 ± 0.8 vs. 14.7 ± 1.0 years; p = 0.001) and AOPSC ≥2.4 (5.4 ± 1.3 vs. 12.0 ± 1.1 years; p < 0.001) had shorter graft survival. CONCLUSION: UK-PSC score at rPSC predicts both graft loss and overall mortality, while AOPSC scores using either age at rPSC or at diagnosis along with severe cholestasis predict graft loss in people with rPSC. These easy-to-administer tools can be utilized in clinical practice to identify high-risk rPSC patients and guide decisions about monitoring/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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.001 |
| 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".