Pediatric Wilson Disease Presenting as Acute Liver Failure
Bibliographic record
Abstract
OBJECTIVES: Wilson disease (WD) presenting as acute liver failure (ALF) is rare and typically fatal without liver transplantation (LT). Its rarity has hindered comprehensive studies. We undertook an individual patient data meta-analysis to characterize a cohort of pediatric patients presenting with ALF whose final diagnosis was WD to examine outcomes and identify predictors of poor outcomes. METHODS: Database searches were conducted in PubMed, ScienceDirect, and Google Scholar, restricted to English-language articles published between January 1984 and May 2018. Articles were excluded if pediatric (<18 years old) data were not extractable or if LT was not readily available at reporting institutions. Extracted data included clinical and biochemical characteristics, genotype, treatment, and outcome. RESULTS: Data were available on 249 subjects from 52 articles, plus 7 additional subjects identified from our institution's WD database (N = 256). Females represented 69% (n = 170/245). Median age at presentation was 13.4 years (n = 204, range 4.0-17.9). Of the total 256 subjects, 87% underwent LT, 11% achieved spontaneous recovery and 2% died before LT. International normalized ratio >2.0 at presentation was a predictor of LT/death (odds ratio 7.6, 95% confidence interval 1.5-28), with a trend observed for hepatic encephalopathy (HE) (odds ratio 4.18, 95% confidence interval 0.99-18). Arithmetic diagnostic scores proved inferior in the pediatric age-bracket compared to adults. CONCLUSIONS: This large international pediatric cohort has permitted an individual patient data analysis of WD presenting as ALF. Notably, 11% of subjects achieved spontaneous survival; the rest required LT. Coagulopathy (international normalized ratio >2:0) and HE at presentation heralded poor outcomes. Further prospective studies may identify additional early predictors of outcomes.
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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 | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.004 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".