Supplementary Material for: Comparative Effectiveness of Mycophenolate Mofetil and Tacrolimus as a Second-line Therapy for Autoimmune Hepatitis: A Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Objective: Autoimmune hepatitis (AIH) is a chronic immune-mediated liver disease that usually responds to corticosteroids ± azathioprine (AZA). However, some patients are intolerant or refractory to first-line therapy and require second-line immunosuppression. Mycophenolate mofetil (MMF) and tacrolimus (TAC) are commonly used alternatives, although comparative evidence is limited. This systematic review and meta-analysis evaluated the efficacy and safety of MMF and TAC in adult AIH patients who failed first-line therapy. Methods: A systematic search of six databases identified 16 eligible studies (n = 705), including retrospective cohorts and one case series. Study quality was assessed using the Newcastle–Ottawa Scale and the MMS (methodological quality and synthesis of case series and case reports) tool. Results: Biochemical remission was achieved in 56% of MMF-treated patients, rising to 66% with ≥6 months follow-up. TAC showed a pooled remission rate of 66%, increasing to 67% when defined by transaminase normalization. MMF was particularly effective in azathioprine-intolerant patients, while TAC showed better outcomes in steroid-refractory patients. Adverse events differed: MMF was most often associated with gastrointestinal intolerance, whereas TAC was linked to tremor, hypertension, diabetes, and renal impairment. However, statistical analysis showed wide confidence intervals, and there was considerable heterogeneity across studies. Conclusion: Both MMF and TAC are effective second-line therapies for AIH. MMF appears safer and better tolerated in azathioprine-intolerant patients, while tacrolimus showed a modest advantage in efficacy over mycophenolate in steroid-refractory cases. Given the limitations of current evidence, including small sample sizes, heterogeneity, and lack of randomized controlled trials, treatment choice should be individualized until higher-quality data are available.
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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 | 0.004 | 0.046 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.005 | 0.008 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.001 |
| Insufficient payload (model declined to judge) | 0.613 | 0.024 |
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".