Primary sclerosing cholangitis and overlap features of autoimmune hepatitis: A coming of age or an age-ist problem?
Bibliographic record
Abstract
Autoimmune liver diseases are siloed into three syndromes that define clinical practice. These classifiers can, and are, challenged by variant presentations across all ages, something inevitable to disease definitions that rely on interpreting (inherently variable) semi-quantitative/qualitative clinical, laboratory, pathological or radiological findings. Furthermore this categorisation is premised on an ongoing absence of definable disease aetiologies. Clinicians thus encounter individuals with biochemical, serological, and histological manifestations that are common to both primary sclerosing cholangitis (PSC) and autoimmune hepatitis (AIH), often labelled as 'PSC/AIH-overlap'. In childhood the term 'autoimmune sclerosing cholangitis (ASC)' may be used, and some propose this to be a distinct disease process. In this article we champion the concept that ASC and PSC/AIH-overlap are not distinct entities. Rather, they represent inflammatory phases of PSC frequently manifesting earlier in the disease course, most notably in younger patients. Ultimately, disease outcomes remain similar to those of a more classical PSC phenotype observed in later life. Thus, we argue that it is now time to align disease names and descriptions used by clinicians across all patient subpopulations, to help unify care. This will enhance collaborative studies and ultimately contribute to rational treatment advances.
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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 | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| 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".