OWE-4 National audit of diagnosis, management and surveillance in primary sclerosing cholangitis in the United Kingdom
Bibliographic record
Abstract
Introduction Primary sclerosing cholangitis (PSC) is a rare disorder and as such clinical care can be heterogeneous. We audited PSC management across the UK against audit standards set by the British Society of Gastroenterology (BSG). Method All UK PSC investigators were invited to complete an electronic questionnaire on the PSC patient cohort encompassing demographics, diagnosis, bowel and biliary tract cancer surveillance, and risk stratification data (March 2019 - Jan 2021). Results 1,795 patients across 30 centres (liver units n = 1548, general gastroenterology units n = 247) were included. Median age at diagnosis was 51 years and 56.4% were men. Magnetic resonance cholangiography (MRCP) was performed as a diagnostic investigation in 1616 patients (90.0%) and 777 (43.3%) had a liver biopsy. Most were monitored by a hepatologist (n = 1610, 89.7%). 931 patients (51.9%) received non-licensed therapy with Ursodeoxycholic acid. 785 patients (43.7%) had not undergone disease staging or risk stratification within the last 2 years; where performed, it was most commonly by transient elastography (n = 645, 78.7%). Surveillance for biliary tract cancer was not undertaken in 515 patients (28.7%); when performed, it was most commonly by ultrasound (US) (n = 568, 47.1%) or alternating MRCP/US (n = 429, n = 35.6%). Ca 19 - 9 was utilised in 730 patients. Concurrent IBD was present in 1264 patients (70.4%) with 256 (20.3%) having had a colectomy. Where classified, pancolitis (Montreal classification E3) was the commonest disease distribution (673/939, 71.7%) with 1.6% (n = 15) having isolated ileal disease. In those without IBD, 142 (28%) patients had not had a colonoscopy and biopsies to exclude diagnosis. Among those with colitis without previous colectomy (n=743), 580 (78.1%) underwent annual colonoscopic surveillance; 30 (5.2%) with dye spray, 230 (39.7%) with biopsies and dye spray, and 252 (43.4%) with protocol biopsies alone. Conclusion There is unwarranted variation in the care of patients with PSC in the UK. In particular relating to risk stratification, exclusion of colitis and surveillance for biliary tract and colonic cancer. The lack of uniformity in clinical practice highlights the need for better education of clinicians about PSC management and the potential role of clinical networks for rare liver diseases within the UK.
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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 | 0.015 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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".