Second‐line Agents in Pediatric Patients With Autoimmune Hepatitis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Ten percent to 20% of children with autoimmune hepatitis (AIH) require second-line therapy to achieve remission. Although current guidelines exist on first-line management, evidence for second-line therapy in treatment-refractory patients is lacking. Our aim was to perform a systematic review and meta-analysis of the efficacy and safety of second-line treatments used in this population. METHODS: Electronic and manual searches were used to identify potential studies for inclusion. Studies were selected based on reported response rates to second-line therapies in children who failed response to prednisone and azathioprine. Data extraction and risk of bias assessment were performed independently by 2 reviewers. Meta-analysis using weighted estimate of response rates at 6 months was performed for each treatment option. Heterogeneity was assessed. RESULTS: Fifteen studies of 76 pediatric patients with AIH were included in the review. Overall response rates at 6 months were estimated as 36% for mycophenolate mofetil (MMF) (N = 34, 95% confidence interval [CI] (16-57)), and 50% for tacrolimus (N = 4, 95% CI (0-100%)) and 83% for cyclosporine (N = 15, 95% CI (66%-100%)). Adverse effects were most frequent with cyclosporine (64% experiencing at least 1 adverse effect) followed by tacrolimus (54%) and MMF (48%). Pooled estimates of adverse events were 78% for cyclosporine (95% CI (54%-100%)), 42% for tacrolimus (95% CI (0%-85%)) and 45% for MMF (95% CI (25%-68%)). Sensitivity analyses were not performed due to small sample size. CONCLUSIONS: Cyclosporine had the highest response rate at 6 months in children with standard-treatment-refractory AIH; however, it also had the highest rate of adverse events. MMF was the second most efficacious option with a low adverse effect rate.
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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 | 0.007 | 0.014 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.005 | 0.009 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".