Enhanced liver fibrosis test predicts transplant‐free survival in primary sclerosing cholangitis, a multi‐centre study
Bibliographic record
Abstract
BACKGROUND & AIMS: Biomarkers reflecting disease activity and prognosis in primary sclerosing cholangitis (PSC) have not been firmly established. Enhanced liver fibrosis (ELF) test was previously reported to predict outcome in PSC. We aimed to validate the prognostic utility of ELF test in an independent, multi-centre, retrospective PSC study population. METHODS: We collected serum samples from PSC patients from seven countries. We estimated rates of transplant-free survival by the Kaplan-Meier method, used Cox proportional hazards regression to explore the association between ELF test and clinical outcome and determined prognostic performance of ELF test by computing the area under the receiver operating characteristic (AUC-ROC) curve. RESULTS: The final analysis included 534 PSC patients (61% males). Features of autoimmune hepatitis or concomitant inflammatory bowel disease affected 44 (8%) and 379 (71%) patients respectively. ELF test levels were higher in patients reaching the combined endpoint liver transplantation or death (median 10.9 [Interquartile range (IQR): 9.8-12.1]; n=24 deaths, 79 liver transplantations) compared to those censored (8.8 [IQR: 8.0-9.8]); P<.001. ELF test expressed as mild, moderate and severe fibrosis was significantly associated with the risk of reaching the endpoint (P<.001). ELF test independently predicted clinical outcome (Hazard ratio 1.31; 95% confidence interval [1.05-1.65]; P=.018), and enabled good discrimination between PSC patients with and without endpoint (AUC-ROC: 0.79). CONCLUSION: Our retrospective data validates the predictive utility of ELF test for clinical outcomes in PSC. The clinical utility of biomarkers for fibrosis in patients with PSC should be assessed in prospective patient cohorts.
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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 | 0.003 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".