Phenotype and Long‐Term Outcome in Recurrent Paediatric Acute Liver Failure: Systematic Review and Individual Participant Data Analysis
Bibliographic record
Abstract
BACKGROUND AND AIMS: Recurrent acute liver failure (RALF) in children is defined as two or more episodes of acute liver failure with complete recovery in between. Several genetic mutations are associated with this condition, including NBAS, RINT1, LARS1 and SCYL1. We have reported liver transplant and mortality rates to help providers make informed management decisions. METHODS: We conducted a systematic review and one-stage individual participant analysis using EMBASE, MEDLINE and Web of Science for English-language studies of RALF occurring in children with NBAS, RINT1, LARS1 or SCYL1 mutations published on or prior to May 14th, 2025. RESULTS: Our search query identified 62 articles, including 38 (66%) case reports, 13 (22%) case series and 6 (11%) cohort studies. A total of 168 (males, 40%) patients were identified, with first presentation at a median age of 9 months (range 1 week-18 years) and preceded by fever in 110/114 (98%) children. The last episode was documented at a median of 4 years (2 months to 21 years). Patients had a median of 4 (range 2-30) episodes, with only 8 (5%) patients experiencing events after age 10 years. Commonest mutated genes identified were NBAS (66%, n = 111) and LARS1 (18.4%, n = 31). Liver transplant was undertaken in 18 (11%) patients, with no recurrences reported post-transplant. Death was reported in 29 (17.3%) patients. CONCLUSIONS: Most patients with RALF present in the first year of life and have a self-limiting course. Episodes of acute liver failure are often associated with febrile illness. Liver transplantation for RALF remains controversial and may be less appealing in etiologies that tend to resolve with age. We recommend whole-exome sequencing with a targeted search for NBAS, SCYL1, RINT1 and LARS1 for patients presenting with unexplained acute liver failure in the setting of a febrile illness.
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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 | 0.020 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.013 |
| Bibliometrics | 0.013 | 0.016 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.002 | 0.002 |
| 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".