Salvage Therapies for Autoimmune Hepatitis: A Critical Review
Bibliographic record
Abstract
Several salvage therapies have been identified for autoimmune hepatitis refractory or recalcitrant to conventional therapy; however, the optimal salvage strategy remains unclear. High-dose prednisolone is currently recommended as the front-line salvage therapy, with alternative immunosuppressive therapies reserved for continuing treatment failure. Of the second-line therapies, the calcineurin inhibitors, cyclosporine and tacrolimus, and mycophenolate mofetil are preferred and have the most accrued clinical data. However, none of these have undergone rigorous clinical evaluation via randomized clinical trials. Tacrolimus is generally preferred over cyclosporine because of its higher potency and increased utility in organ transplantation. Mycophenolate is particularly useful for azathioprine intolerance but also for nonresponse to standard treatment. Subjects with progressive liver failure should undergo liver transplantation evaluation. The appropriate timing, dosing, and monitoring of salvage therapies require determination. Several promising immunosuppressive therapies have been developed for autoimmune diseases including molecular agents that may enhance regulatory T cell activity and function.
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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.006 | 0.001 |
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".