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Record W4230913005 · doi:10.1002/9781119532637.ch6

Autoimmune Hepatitis

2020· other· fi· W4230913005 on OpenAlexaff
Aliya Gulamhusein, Patrick McKiernan

Bibliographic record

Venuenot available
Typeother
Languagefi
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsToronto General HospitalUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsAutoimmune hepatitisMedicineAzathioprineImmunosuppressionHypergammaglobulinemiaPrimary sclerosing cholangitisImmunologyHepatitisInduction therapyPrednisoloneMaintenance therapyAutoantibodyInternal medicineGastroenterologyDiseaseChemotherapy

Abstract

fetched live from OpenAlex

Autoimmune hepatitis (AIH) is a progressive inflammatory hepatopathy characterized by hypergammaglobulinemia, specific autoantibodies, interface hepatitis, and normal cholangiography in the absence of viral infection. AIH is rare in both adults and children though epidemiologic data are limited and may be biased by underrecognition, particularly in underdeveloped countries. Histologic evaluation is required for the diagnosis of AIH. Unique to childhood is the particularly challenging issue of distinguishing between AIH and autoimmune sclerosing cholangitis. Longterm immunosuppression is the cornerstone of therapy in pediatric and adult AIH and requires corticosteroid based induction followed by, ideally, steroid sparing maintenance of remission with azathioprine. Firstline therapy relies on corticosteroid based induction usually at a daily dose of 0.5 mg/kg, though effective use of lower doses has been suggested. Secondline therapy may need to be considered in 10–15% of patients as a result of either intolerance or suboptimal response to standard regimens.

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 machine prediction

Teacher imitation

Not 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.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.002
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Other · Consensus signal: Other
Teacher disagreement score0.083
Threshold uncertainty score0.276

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.002
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.001
Science and technology studies0.0010.000
Scholarly communication0.0010.000
Open science0.0000.001
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0830.041

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 head0.015
GPT teacher head0.245
Teacher spread0.230 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreOther

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

Citations0
Published2020
Admission routes1
Has abstractyes

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