Bibliographic record
Abstract
Abstract Autoimmune hepatitis (AIH) is an organ‐specific autoimmune disease resulting from a loss of immunological tolerance against hepatocyte autoantigens. In the absence of treatment, this disease is fatal. Distinctive characteristics of AIH include hypergammaglobulinemia, presence of autoantibodies and interface hepatitis with lymphoplasmacytic infiltrates. AIH is a rare disease with an estimated point prevalence in Europe and North America of 50–200 cases per million. The pathogenesis of AIH remains unknown. AIH is believed to result from a combination of liver‐specific susceptibility factors, faulty immune tolerance mechanisms (central and peripheral) as well as host and environmental susceptibility factors. Patients respond well to immunosuppressive treatment, but 10–20% of patients are unable to achieve remission using standard treatment. Through a better understanding of AIH pathogenesis as well as cellular(s) and molecular(s) effectors involved, new and effective site‐specific immunotherapies are being developed to induce long‐term remission in patients with minimal deleterious side effects. Key Concepts Autoimmune hepatitis is characterised by a loss of tolerance against hepatocytes leading to the progressive destruction of the hepatic parenchyma. About 70–90% of AIH patients are women. Hallmark characteristics of AIH include hypergammaglobulinemia, presence of autoantibodies and an interface hepatitis with lymphoplasmacytic infiltrates. Type 1 AIH is characterised by the presence of ANA and/or SMA autoantibodies while Type 2 AIH is characterised by the presence of anti‐LKM1 and/or anti‐LC1 autoantibodies. There is a genetic association between AIH and HLA class II genes suggesting a role for CD4+T cells autoimmune response in AIH pathogenesis. Conventional therapy for AIH patients consists of nonspecific immunosuppression, usually through the use of a combination of prednisone/prednisolone and azathioprine. New biologics‐based specific immunotherapies are currently in development and aim to induce long‐term remission with minimal unwanted side effects.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.099 | 0.049 |
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".