A real‐world study focused on the long‐term efficacy of mycophenolate mofetil as first‐line treatment of autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND: Front-line therapy with mycophenolate mofetil (MMF) in autoimmune hepatitis (AIH) has shown high on-treatment remission rates. AIM: To study prospectively in a real-world fashion the long-term outcome of a large group of consecutive treatment-naïve AIH patients. METHODS: Between 2000 and 2014, 158 patients were recruited but only 131 were eligible for treatment (109 MMF/prednisolone; 22 prednisolone ± azathioprine). Long-term data on outcome after drug withdrawal were evaluated. Patients stopped treatment after having achieved complete response (normal transaminases and IgG) for at least the last 2 years. RESULTS: At diagnosis, 31.6% of patients had cirrhosis and 72.8% insidious presentation. A total of 102 of 109 (93.6%) responded initially to MMF within 2 (1-18) months. A total of 78 of 109 (71.6%) had complete response on treatment and 61 of 78 (78.2%) maintained remission off prednisolone. MMF-treated patients had increased probability of complete response compared to those receiving azathioprine (P = 0.03). Independent predictors of complete response were lower ALT at 6 months (P = 0.001) and acute presentation (P = 0.03). So far, treatment withdrawal was feasible in 40/109 patients and 30 (75%) are still in remission after 24 (2-129) months. Remission maintenance was associated with longer MMF treatment (P = 0.005), higher baseline ALT (P < 0.02), lower IgG on 6 months (P = 0.004) and histological improvement. CONCLUSIONS: Mycophenolate mofetil proved to be an efficient first-line treatment for AIH, achieving so far the highest rates of remission maintenance off treatment (75%) ever published for at least a median of 2 years, although the remission criteria used were strict. However, the risk of potential bias and overestimation of intervention benefits from MMF cannot be completely excluded as this is a real world and not a randomised controlled trial.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".