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Record W2169359081 · doi:10.1177/0961203308100559

Autoimmune hepatitis and juvenile systemic lupus erythematosus

2009· article· en· W2169359081 on OpenAlexfundno aff
MEJ Deen, Gilda Porta, FJ Fiorot, LMA Campos, AME Sallum, CAA Silva

Bibliographic record

VenueLupus · 2009
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsMedicineAutoimmune hepatitisAzathioprinePrednisoneInternal medicineLiver biopsyPopulationRheumatologyHepatitisGastroenterologySystemic lupus erythematosusHepatologyPediatricsBiopsyDisease

Abstract

fetched live from OpenAlex

Juvenile systemic lupus erythematosus (JSLE) and autoimmune hepatitis (AIH) are both autoimmune disorders that are rare in children and have a widespread clinical manifestation. A few case reports have shown a JSLE-AIH associated disorder. To our knowledge, this is the first study that simultaneously evaluated the prevalence of JSLE-AIH in a large JLSE and AIH population in groups of Hepatology and Rheumatology of a tertiary Paediatric University Hospital. In a 24-year period, 228 patients were diagnosed with JSLE (ACR criteria). In the same period, 252 patients were diagnosed with AIH according to the International Autoimmune Hepatitis Group. In this article, we present the demographic data, clinical features, laboratory exams and treatment of four children with both the diseases. The prevalence was 1.8% in JSLE population and was 1.6% in AIH population. The current median age was 15.5 years and three were females. In three of them, the diagnosis of AIH preceded JSLE. All of them had increased liver enzymes with a characteristic liver biopsy of AIH and responded to the combination of prednisone, azathioprine and antimalarial drugs. In conclusion, the presence of AIH-JSLE associated disorder was rarely observed. The liver biopsy could be necessary in patients with JLSE with a persistent increase of liver enzymes.

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 score0.048
Threshold uncertainty score0.451

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.009
GPT teacher head0.236
Teacher spread0.227 · 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

Citations33
Published2009
Admission routes1
Has abstractyes

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