A333 THE ROLE OF IMAGING IN DETERMINING PROGNOSIS FOR PRIMARY SCLEROSING CHOLANGITIS: A SYSTEMATIC REVIEW
Bibliographic record
Abstract
Primary Sclerosing Cholangitis (PSC) is a chronic, progressive, inflammatory bile duct disease that causes fibrosis and stricturing. Resultant complications include life–threatening infection, progressive liver disease including cirrhosis, and malignant tumors. While diagnosis utilizes imaging studies extensively, the role of imaging in determining the clinical prognosis is less clear. The aim of this study was to systematically review existing imaging indices and features that predict PSC progression. We performed a systematic review of imaging indices and features that predict PSC progression. PubMed, EMBASE (Ovid), MEDLINE (Ovid), and the Cochrane Library (CENTRAL) from inception to November 2015 were searched for relevant studies. Pertinent data was extracted and assessed. The search returned 2024 results. Of the resulting twenty-five pertinent studies selected for full text review, eight were included. The two imaging modalities studied were endoscopic retrograde cholangiopancreatography (ERCP) and magnetic resonance imaging. Two imaging indices (ERCP based and MR based) have been described and partially validated. The ERCP index was validated in a second population by comparing predicted time to liver related death or liver transplant to actual outcomes. It was then updated to be more robust. The MRCP index determined via multivariate analysis for gadolinium and non-gadolium studies was found to only be predictive of transplant free survival for the non-gadolinium studies in a proof-of-concept cohort. Two imaging indices, one ERCP and one MR based, have been described to predict prognosis. The ERCP index has been validated in a second cohort while the MRCP index requires external validation. None
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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 | 0.007 | 0.041 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.008 | 0.007 |
| Bibliometrics | 0.012 | 0.013 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.010 | 0.001 |
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".