The Complementary Value of Magnetic Resonance Imaging and Vibration-Controlled Transient Elastography for Risk Stratification in Primary Sclerosing Cholangitis
Bibliographic record
Abstract
OBJECTIVES: Magnetic resonance (MR) risk scores and liver stiffness (LS) have individually been shown to predict clinical outcomes in primary sclerosing cholangitis (PSC). The aim of this study was to assess their complementary prognostic value. METHODS: Patients with PSC from 3 European centers with a 3-dimensional MR cholangiography available for central reviewing and a valid LS measurement assessed by vibration-controlled transient elastography by FibroScan performed within a 6-month interval were included in a longitudinal retrospective study. The MR score (Anali) without gadolinium (Gd) was calculated according to the formula: (1 × dilatation of intrahepatic bile ducts) + (2 × dysmorphy) + (1 × portal hypertension). The primary end point was survival without liver transplantation or cirrhosis decompensation. The prognostic values of LS and Anali score without Gd were assessed using Cox proportional hazard models. RESULTS: One hundred sixty-two patients were included. Over a total follow-up of 753 patient-years, 40 patients experienced an adverse outcome (4 liver transplantations, 6 liver-related deaths, and 30 cirrhosis decompensations). LS and Anali score without Gd were significantly correlated (ρ = 0.51, P < 0.001) and were independently associated with the occurrence of an adverse outcome. Optimal prognostic thresholds were 10.5 kPa for LS and 2 for the Anali score without Gd. Hazard ratios (95% confidence interval) were 2.07 (1.06-4.06) and 3.78 (1.67-8.59), respectively. The use in combination of these 2 thresholds allowed us to separate patients into low-, medium-, and high-risk groups for developing adverse outcomes. The 5-year cumulative rates of adverse outcome in these 3 groups were 8%, 16%, and 38% (P < 0.001), respectively. DISCUSSION: The combined use of MRI and vibration-controlled transient elastography permits easy risk stratification of patients with PSC.
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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 | 0.010 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".