Autoimmune hepatitis: Towards a personalized treatment
Bibliographic record
Abstract
Autoimmune hepatitis is an uncommon condition that affects both adults and children and is characterized by chronic and recurrent inflammatory activity in the liver. This inflammation is accompanied by elevated IgG and autoantibody levels. Historically, treatment consists of steroids with the addition of azathioprine, which results in remission in approximately 80% of patients. Despite significant advancements in our understanding of the immune system over the past two decades, few modifications have been made to treatment algorithms, which have remained largely unchanged since they were first proposed more than 40 years ago. This review summarized the various treatment options currently available as well as our experiences using them. Although steroids are the standard treatment for induction therapy, other medications may be considered. Cyclosporin A, a calcineurin inhibitor that decreases T cell activation, has proven effective for induction of remission, but its long-term side effects limit its appeal for maintenance. Tacrolimus, a drug belonging to the same family, has been used in patients with refractory diseases with fewer side effects. Sirolimus and everolimus have interesting effects on regulatory T cell populations and may become viable options in the future. Mycophenolate mofetil is not effective for induction but is a valid alternative for patients who are intolerant to azathioprine. B cell-depleting drugs, such as rituximab and belimumab, have been successfully used in refractory cases and are useful in both the short and long term. Other promising treatments include anti-tumor necrosis factors, Janus kinases inhibitors, and chimeric antigen receptor T cell therapy. This growing armamentarium allows us to imagine a more tailored approach to the treatment of autoimmune hepatitis in the near future.
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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.003 | 0.008 |
| Insufficient payload (model declined to judge) | 0.007 | 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".