Review article: the modern management of autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND: The management of autoimmune hepatitis (AIH) continues to be refined. However, several issues remain unresolved, primarily as a consequence of the low incidence of the disease. This factor has contributed both to a lack of understanding of and a paucity of large scale clinical trials involving therapeutic agents. AIM: To summarize the latest evidence regarding the pathogenesis, diagnosis, therapy and long-term management of AIH with a focus on clinical aspects of the disease. METHOD: We searched PUBMED for articles pertaining to AIH, its pathogenesis, treatment and clinical outcomes, combined with the authors' own knowledge of the literature. RESULTS: Standard therapy (corticosteroids and azathioprine) is effective in more than 80% of patients which renders study of novel agents difficult. Budesonide appears to show equivalence to prednisolone. Available, but limited, data suggest that mycophenolate mofetil, tacrolimus and ciclosporin are all variably effective second line agents. Patients with AIH and cirrhosis are at risk of hepatocellular carcinoma (HCC) and require screening. Patients with end stage liver disease represent excellent candidates for liver transplantation. CONCLUSIONS: Despite ongoing limitations in the understanding of pathogenesis and difficulties in evaluating novel therapies, the management of AIH continues to evolve slowly. Multi-centre collaboration is necessary to obtain sufficient patient numbers to undertake good quality therapeutic studies.
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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.003 |
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".