Bibliographic record
Abstract
PURPOSE OF REVIEW: Autoimmune hepatitis (AIH) is a chronic, progressive hepatitis of uncertain cause which has fluctuating activity characterized by periods of flares and remissions. Initial placebo-controlled trials carried out in the 1970s demonstrated that immunosuppression with steroids was extremely effective in reducing flares and progression of disease. The late 1980s-1990s could be described as the 'Dark Ages' of AIH treatment research. Very few clinical studies were performed during this time, although it became increasingly apparent that not all patients tolerated or responded to traditional immunosuppression, and that not all patients were easy to diagnose because of overlapping features with other autoimmune conditions. Fortunately, clinical research in the treatment of AIH has experienced a renaissance in the 21st century. RECENT FINDINGS: This review highlights some of the more important recent discoveries, including the creation of the clinically useful short form of the autoimmune hepatitis diagnostic scoring system; accumulation of data supporting the use of mycophenolate and tacrolimus as second-line treatment; and the recent completion of the largest, double-blind, placebo-controlled trial of AIH treatment to date, comparing budesonide to prednisone. SUMMARY: These new findings are pertinent to the everyday clinical management of patients with AIH.
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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 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) | 0.005 | 0.004 |
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".