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Record W4385683800 · doi:10.1097/hc9.0000000000000218

Distinct immune surveillance in primary biliary cholangitis and primary sclerosing cholangitis is linked with discrete cholangiocarcinoma risk

2023· article· en· W4385683800 on OpenAlexafffund
Roopa Hebbandi Nanjundappa, Urs Christen, Channakeshava Sokke Umeshappa

Bibliographic record

VenueHepatology Communications · 2023
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsIzaak Walton Killam Health CentreDalhousie University
FundersFaculty of Medicine, Dalhousie UniversityCanadian Institutes of Health ResearchDalhousie University
KeywordsPrimary sclerosing cholangitisMedicineGastroenterologyMalignancyInternal medicineIntrahepatic bile ductsBile ductPrimary biliary cirrhosisIncidence (geometry)Intrahepatic CholangiocarcinomaImmune systemDiseaseImmunology

Abstract

fetched live from OpenAlex

Primary biliary cholangitis (PBC) and primary sclerosing cholangitis (PSC) are 2 major liver autoimmune diseases. PBC is common in women and primarily affects intrahepatic small bile duct epithelial cells, known as cholangiocytes. In contrast, PSC is dominant in men and primarily affects medium and big intrahepatic and extrahepatic bile duct epithelial cells. Cholangiocarcinoma (CCA) is a malignancy arising from cholangiocytes, and its incidence is increasing worldwide in both men and women. Numerous retrospective and clinical studies have suggested that PBC patients rarely develop CCA compared to PSC patients. CCA is accountable for the higher deaths in PSC patients due to ineffective therapies and our inability to diagnose the disease at an early stage. Therefore, it is paramount to understand the differences in immune surveillance mechanisms that render PBC patients more resistant while PSC patients are susceptible to CCA development. Here, we review several potential mechanisms contributing to differences in the susceptibility to CCA in PBC versus PSC patients.

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)0.000
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: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.004
Threshold uncertainty score0.890

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
Scholarly communication0.0000.000
Open science0.0000.001
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 head0.025
GPT teacher head0.266
Teacher spread0.241 · 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 designObservational
Domainnot available
GenreEmpirical

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

Citations8
Published2023
Admission routes2
Has abstractyes

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