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Record W4323044247 · doi:10.1016/j.jhep.2023.02.030

Primary sclerosing cholangitis and overlap features of autoimmune hepatitis: A coming of age or an age-ist problem?

2023· review· en· W4323044247 on OpenAlexaff
Amanda Ricciuto, Binita M. Kamath, Gideon M. Hirschfield, Palak Trivedi

Bibliographic record

VenueJournal of Hepatology · 2023
Typereview
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsToronto Liver CentreUniversity of TorontoHospital for Sick Children
Fundersnot available
KeywordsPrimary sclerosing cholangitisAutoimmune hepatitisMedicineDiseasePathologicalOverlap syndromePancaLiver diseaseImmunologyPathologyInternal medicine

Abstract

fetched live from OpenAlex

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.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.801
Threshold uncertainty score0.872

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0040.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.084
GPT teacher head0.359
Teacher spread0.274 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designOther design
Domainnot available
GenreReview

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".

Quick stats

Citations59
Published2023
Admission routes1
Has abstractyes

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