Novel Immunotherapies for Autoimmune Hepatitis
Bibliographic record
Abstract
Autoimmune hepatitis (AIH) is a multifactorial autoimmune disease of unknown pathogenesis, characterized by a loss of immunological tolerance against liver autoantigens resulting in the progressive destruction of the hepatic parenchyma. Current treatments are based on non-specific immunosuppressive drugs. Although tremendous progress has been made using specific biological agents in other inflammatory diseases, progress has been slow to come for autoimmune hepatitis patients. While current treatments are successful in the majority of patients, treatment discontinuation is difficult to achieve and relapses are frequent. Lifelong immunosuppression is not without risks, especially in the pediatric population; 4% of patient with type 1 AIH will eventually develop hepatocellular carcinoma with a 2.9% probability after 10 years of treatment. Therefore, future treatments should aim to restore tolerance to hepatic autoantigens and induce long-term remission. Promising new immunotherapies have been tested in experimental models of autoimmune hepatitis including T and B cell depletion and regulatory CD4+ T cells infusion. Clinical studies on limited numbers of patients have also shown encouraging results using B-cell depleting (Rituximab) and anti-TNF-alpha (Infliximab) antibodies. A better understanding of key molecular targets in AIH combined with effective site-specific immunotherapies could lead to long-term remission without blanket immunosuppression and with minimal deleterious side effects.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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.
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 teacher head, 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".