Autoimmune hepatitis: From current knowledge and clinical practice to future research agenda
Bibliographic record
Abstract
Autoimmune hepatitis is a chronic inflammatory liver disease. Unknown triggers lead to a mainly T cell-mediated immune response targeting the liver, the main auto-antigen of which has not been identified yet. The diagnosis of autoimmune hepatitis is based on the elevation of immunoglobulin G/hypergammaglobulinemia, detection of characteristic autoantibodies as well as a typical pattern on liver histology. Exclusion of other causes of hepatitis and response to immunosuppressive treatment support the diagnosis of autoimmune hepatitis. The mainstay of autoimmune hepatitis treatment has, from its first description to the current time, consisted of predniso(lo)ne to induce remission, in combination with azathioprine, which is used to maintain it. Nonetheless, side effects and non-response with ongoing inflammation despite standard therapy demand treatment alternatives. Only through a better understanding of the pathogenesis of autoimmune hepatitis can a more selective and effective treatment be offered to patients in the future. Until this goal is reached, improvement of diagnostic approaches and optimization of current therapy rank highest on the research agenda for autoimmune hepatitis.
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 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 | 0.002 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.005 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.002 | 0.004 |
| Insufficient payload (model declined to judge) | 0.008 | 0.002 |
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".