Quantitative <scp>MRCP</scp> and metrics of bile duct disease over time in patients with primary sclerosing cholangitis: A prospective study
Bibliographic record
Abstract
BACKGROUND: Imaging markers of biliary disease in primary sclerosing cholangitis (PSC) have potential for use in clinical and trial disease monitoring. Herein, we evaluate how quantitative magnetic resonance cholangiopancreatography (MRCP) metrics change over time, as per the natural history of disease. METHODS: Individuals with PSC were prospectively scanned using non-contrast MRCP. Quantitative metrics were calculated using MRCP+ post-processing software to assess duct diameters and dilated and strictured regions. Additionally, a hepatopancreatobiliary radiologist (blinded to clinical details, biochemistry and quantitative biliary metrics) reported each scan, including ductal disease assessment according to the modified Amsterdam Cholangiographic Score (MAS). RESULTS: At baseline, 14 quantitative MRCP+ metrics were found to be significantly different in patients with PSC (N = 55) compared to those with primary biliary cholangitis (N = 55), autoimmune hepatitis (N = 57) and healthy controls (N = 18). In PSC specifically, baseline metrics quantifying the number of strictures and the number and length of bile ducts correlated with the MAS, transient elastography and serum ALP values (p < 0.01 for all correlations). Over a median 371-day follow-up (range: 364-462), 29 patients with PSC underwent repeat MRCP, of whom 15 exhibited quantitative changes in MRCP+ metrics. Compared to baseline, quantitative MRCP+ identified an increasing number of strictures over time (p < 0.05). Comparatively, no significant differences in biochemistry, elastography or the MAS were observed between timepoints. Quantitative MRCP+ metrics remained stable in non-PSC liver disease. CONCLUSION: Quantitative MRCP+ identifies changes in ductal disease over time in PSC, despite stability in biochemistry, liver stiffness and radiologist-derived cholangiographic assessment (trial registration: ISRCTN39463479).
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| 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".