Impact on follow‐up strategies in patients with primary sclerosing cholangitis
Bibliographic record
Abstract
BACKGROUND & AIMS: Evidence for the benefit of scheduled imaging for early detection of hepatobiliary malignancies in primary sclerosing cholangitis (PSC) is limited. We aimed to compare different follow-up strategies in PSC with the hypothesis that regular imaging improves survival. METHODS: We collected retrospective data from 2975 PSC patients from 27 centres. Patients were followed from the start of scheduled imaging or in case of clinical follow-up from 1 January 2000, until death or last clinical follow-up alive. The primary endpoint was all-cause mortality. RESULTS: A broad variety of different follow-up strategies were reported. All except one centre used regular imaging, ultrasound (US) and/or magnetic resonance imaging (MRI). Two centres used scheduled endoscopic retrograde cholangiopancreatography (ERCP) in addition to imaging for surveillance purposes. The overall HR (CI95%) for death, adjusted for sex, age and start year of follow-up, was 0.61 (0.47-0.80) for scheduled imaging with and without ERCP; 0.64 (0.48-0.86) for US/MRI and 0.53 (0.37-0.75) for follow-up strategies including scheduled ERCP. The lower risk of death remained for scheduled imaging with and without ERCP after adjustment for cholangiocarcinoma (CCA) or high-grade dysplasia as a time-dependent covariate, HR 0.57 (0.44-0.75). Hepatobiliary malignancy was diagnosed in 175 (5.9%) of the patients at 7.9 years of follow-up. Asymptomatic patients (25%) with CCA had better survival if scheduled imaging had been performed. CONCLUSIONS: Follow-up strategies vary considerably across centres. Scheduled imaging was associated with improved survival. Multiple factors may contribute to this result including early tumour detection and increased endoscopic treatment of asymptomatic benign biliary strictures.
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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 | 0.016 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| 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".