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Record W2043475483 · doi:10.1055/s-0029-1233536

New Animal Models for Autoimmune Hepatitis

2009· review· en· W2043475483 on OpenAlex

Why this work is in the frame

A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.

fundA Canadian funder is recorded on the work.
no affNo Canadian affiliation: this work is invisible to an affiliation-only frame.
No Canadian affiliation. An affiliation-only frame, the usual design, would never have seen this work. It is one of the works that make the case for inverting the frame.

Bibliographic record

VenueSeminars in Liver Disease · 2009
Typereview
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
FundersNational Institute of Diabetes and Digestive and Kidney DiseasesAlberta Innovates - Health Solutions
KeywordsAutoimmune hepatitisMedicineImmunologyHepatitisLiver diseaseDiseaseAutoimmune diseaseEtiologyImmune systemAnimal modelPathologyInternal medicine

Abstract

fetched live from OpenAlex

Autoimmune hepatitis (AIH) is often diagnosed late in the disease course and usually requires lifelong immunosuppressive therapy. Unfortunately, the etiology of the disease and the mechanisms leading to the autoimmune destruction of the liver parenchyma are only poorly understood. For a long time, one reason for this lack of apprehension was the absence of reliable animal models with a chronic immune response against liver tissues. Initial attempts to break tolerance against hepatocytes usually just resulted in mild, transient hepatitis flares. Recently, however, some approaches have been made to establish models of chronic AIH that reflect the immunopathogenic mechanisms seen in humans. In this article, we reflect on recent models, focusing on their feasibility and chances for success in providing a platform for studying the mechanisms of autoimmune liver destruction and the development of possible therapeutic interventions.

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.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.961
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.045
GPT teacher head0.328
Teacher spread0.283 · 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