Systematic review: managing suboptimal treatment responses in autoimmune hepatitis with conventional and nonstandard drugs
Bibliographic record
Abstract
BACKGROUND: Corticosteroid treatment for autoimmune hepatitis has been shown by randomised controlled clinical trials to ameliorate symptoms, normalise liver tests, improve histological findings and extend survival. Nevertheless, suboptimal responses to corticosteroid treatment still occur. AIM: To describe the current definitions, frequencies, clinical relevance and treatment options for suboptimal responses, and to discuss alternative medications that have been used off-label for these occurrences. METHODS: Literature search was made for full-text papers published in English using the keyword 'autoimmune hepatitis'. Authors' personal experience and investigational studies also helped to identify important contributions to the literature. RESULTS: Suboptimal responses to standard therapy include treatment failure (7%), incomplete response (14%), drug toxicity (13%) and relapse after drug withdrawal (50-86%). The probability of a suboptimal response prior to treatment is higher in young patients and in patients with a severe presentation, jaundice, high MELD score at diagnosis, multilobular necrosis or cirrhosis, antibodies to soluble liver antigen, or inability to improve by clinical indices within two weeks or by MELD score within 7 days of conventional corticosteroid treatment. Management strategies have been developed for the adverse responses and nonstandard drugs, including mycophenolate mofetil, budesonide, ciclosporin, tacrolimus, sirolimus and rituximab, are emerging as rescue therapies or alternative frontline agents. CONCLUSIONS: Once diagnosed, the suboptimal response should be treated by a highly individualised and well-monitored regimen, preferentially using first-line therapy. Nonstandard drugs warrant consideration as salvage or second-line therapies.
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 | 0.012 | 0.061 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.010 | 0.007 |
| Bibliometrics | 0.010 | 0.010 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.000 |
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".