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Autoimmune hepatitis in childhood

2007· article· en· W1972639943 on OpenAlexfundno aff
Tomoo Fujisawa, Tsuyoshi Sogo, Haruki Komatsu, Ayano Inui

Bibliographic record

VenueHepatology Research · 2007
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsAutoimmune hepatitisMedicineAutoantibodyPathologicalImmunologyHuman leukocyte antigenEtiologyGenetic predispositionIncidence (geometry)HepatitisDiseaseAntibodyInternal medicineAntigen

Abstract

fetched live from OpenAlex

Autoimmune hepatitis (AIH) is a generally progressive inflammatory liver disease of unknown etiology that occurs in adults and children. Although the peak age of incidence is in prepubertal girls, AIH has been diagnosed as early 6 months of age. Two types of AIH are recognized according to the nature of the autoantibody detected in children at the time of diagnosis. The diagnosis of AIH is based on histological abnormalities, characteristic clinical and biochemical findings, and abnormal levels of serum globulin due almost exclusively to markedly increased immunoglobulin G including various autoantibodies. A genetic predisposition is suggested by the increase frequency of human leukocyte antigen (HLA) haplotypes HLA-B8/DR3, and allotypes DR3 and DR4. However, the incidence of these HLA types is different in each country.Proposed triggers of AIH that may initiate the inflammatory process include some viral infections and some drugs. There is much information about adults with AIH, however, little information on AIH in children, especially in Japanese children. To clarify the clinicohistological features of AIH in Japanese children, we analyzed the clinical, pathological features and response to treatment in 12 Japanese children with AIH. Furthermore, we discuss several problems for diagnosis and treatment for AIH in children.

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)0.002
metaresearch head score (Gemma)0.001
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 score0.017
Threshold uncertainty score0.832

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.062
GPT teacher head0.408
Teacher spread0.347 · 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

Citations21
Published2007
Admission routes1
Has abstractyes

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