Outcomes and management in paediatric autoimmune hepatitis presenting as acute liver failure: Individual patient data meta‐analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Autoimmune hepatitis (AIH) in children presenting in acute liver failure (ALF) can be fatal and often requires liver transplantation (LTx). This individual patient data meta-analysis (IPD) aims to examine management and outcomes of this population, given the lack of large cohort studies on paediatric AIH first presenting as ALF (AIH-ALF). METHODS: A systematic review was conducted in accordance with the Preferred Reporting Items for Systematic Review and Meta-Analyses of IPD statement using PubMed and Excerpta Medica dataBASE, and included English studies published between 2000 and 2020. The study included patients under 21 years of age, diagnosed with type 1 or 2 AIH and presenting with ALF. Data extracted included clinical and biochemical characteristics, interventions, and outcomes. RESULTS: Three hundred and thirty eligible patients from 61 studies were identified, with an additional five patients from our institution. The majority were female (66.8%), with a median age of 10. Overall, 59.7% achieved native liver survival (NLS), 35% underwent LTx, and 5% died before LTx. The use of corticosteroids with non-steroid immunomodulators increased the likelihood of NLS by 2.5-fold compared to corticosteroids alone. AIH-1 was associated with 3.3-fold odds for NLS, compared to AIH-2. However, on multivariate analysis, only AIH-1 was identified as an independent predictor for NLS (OR 3.8 [95% CI 1.03-14.2], p = .04). CONCLUSION: While corticosteroids and non-steroid immunomodulators treatment may offer enhanced probability of achieving NLS, treatment regimens for AIH-ALF may need to consider patient-specific factors, especially AIH type. This highlights the potential for NLS in AIH-ALF and suggest a need to identify biomarkers which predict the need for combination immunosuppression to avoid LTx.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.052 |
| Bibliometrics | 0.004 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".