Per-oral cholangioscopy in patients with primary sclerosing cholangitis: a 12-month follow-up study
Bibliographic record
Abstract
Abstract Background and study aims Patients with primary sclerosing cholangitis (PSC) have a 9% to 20% lifetime incidence of cholangiocarcinoma (CCA). Per-oral cholangioscopy (POCS) added to endoscopic retrograde cholangiography (ERC) could potentially improve detection of CCA occurrence. We prospectively assessed POCS identification of 12-month CCA incidence in PSC patients undergoing ERC. Patients and methods Consecutive patients with PSC, an indication for ERC, and no prior liver transplantation were enrolled. During the index procedure, POCS preceded planned therapeutic maneuvers. The primary endpoint was ability for POCS visualization with POCS-guided biopsy to identify CCA during 12-month follow-up. Secondary endpoints included ability of ERC/cytology to identify CCA, repeat ERC, liver transplantation, and serious adverse events (SAEs). Results Of 42 patients enrolled, 36 with successful cholangioscope advancement were analyzed. Patients had a mean age 43.5±15.6 years and 61% were male. Three patients diagnosed with CCA had POCS visualization impressions of benign/suspicious/suspicious, and respective POCS-guided biopsy findings of suspicious/positive/suspicious for malignancy at the index procedure. The three CCA cases had ERC visualization impressions of benign/benign/suspicious, and respective cytology findings of atypical/atypical/suspicious for malignancy. No additional patients were diagnosed with CCA during median 11.5-month follow-up. Twenty-three repeat ERCs (5 including POCS) were performed in 14 patients. Five patients had liver transplantation, one after CCA diagnosis and four after benign cytology at the index procedure. Three patients (7.1%) had post-ERC pancreatitis. No SAEs were POCS-related. Conclusions In PSC patients, POCS visualization/biopsy and ERC/cytology each identified three cases of CCA. Some patients had a repeat procedure and none experienced POCS-related SAEs.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| 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".