Inflammatory Bowel Disease and Primary Sclerosing Cholangitis: A Review of the Phenotype and Associated Specific Features
Bibliographic record
Abstract
Primary sclerosing cholangitis (PSC) is a chronic, progressive cholestatic disease that is associated with inflammatory bowel disease (IBD) in approximately 70% of cases. Although the pathogenesis is still unknown for both diseases, there is increasing evidence to indicate that they share a common underlying predisposition. Herein, we review the epidemiology, diagnosis, disease pathogenesis, and specific clinical features of the PSC-IBD phenotype. Patients with PSC-IBD have a distinct IBD phenotype with an increased incidence of pancolitis, backwash ileitis, and rectal sparing. Despite often having extensive colonic involvement, these patients present with mild intestinal symptoms or are even asymptomatic, which can delay the diagnosis of IBD. Although the IBD phenotype has been well characterized in PSC patients, the natural history and disease behavior of PSC in PSC-IBD patients is less well defined. There is conflicting evidence regarding the course of IBD in PSC-IBD patients who receive liver transplantation and their risk of recurrent PSC. IBD may also be associated with an increased risk of cholangiocarcinoma in PSC patients. Overall, the PSC-IBD population has an increased risk of developing colorectal neoplasia compared to the conventional IBD population. Lifelong annual surveillance colonoscopy is currently recommended.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".