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Record W1974136917 · doi:10.3748/wjg.v20.i35.12662

Association of autoimmune hepatitis and systemic lupus erythematodes: A case series and review of the literature

2014· review· en· W1974136917 on OpenAlexfundno aff
Claudia Beisel

Bibliographic record

VenueWorld Journal of Gastroenterology · 2014
Typereview
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsnot available
FundersAlberta Innovates - Health Solutions
KeywordsAutoimmune hepatitisMedicineHepatitisSystemic lupus erythematosusLiver diseaseDiseaseAutoimmune diseaseImmunologyViral hepatitisInternal medicine

Abstract

fetched live from OpenAlex

Liver test abnormalities have been described in up to 60% of patients with systemic lupus erythematodes (SLE) at some point during the course of their disease. Prior treatment with potentially hepatotoxic drugs or viral hepatitis is commonly considered to be the main cause of liver disease in SLE patients. However, in rare cases elevated liver enzymes may be due to concurrent autoimmune hepatitis (AIH). To distinguish whether the patient has primary liver disease with associated autoimmune clinical and laboratory features resembling SLE - such as AIH - or the elevation of liver enzymes is a manifestation of SLE remains a difficult challenge for the treating physician. Here, we present six female patients with complex autoimmune disorders and hepatitis. Patient charts were reviewed in order to investigate the complex relationship between SLE and AIH. All patients had coexisting autoimmune disease in their medical history. At the time of diagnosis of AIH, patients presented with arthralgia, abdominal complaints, cutaneous involvement and fatigue as common symptoms. All patients fulfilled the current diagnostic criteria of both, AIH and SLE. Remission of acute hepatitis was achieved in all cases after the initiation of immunosuppressive therapy. In addition to this case study a literature review was conducted.

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)0.001
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: Systematic review · Consensus signal: Systematic review
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.303
Threshold uncertainty score0.509

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.267
Teacher spread0.258 · 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 designSystematic review
Domainnot available
GenreReview

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

Citations64
Published2014
Admission routes1
Has abstractyes

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