Association of autoimmune hepatitis and systemic lupus erythematodes: A case series and review of the literature
Bibliographic record
Abstract
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.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot 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.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.006 | 0.005 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".