Prevalence and Significance of Autoantibodies in Children With Acute Liver Failure
Bibliographic record
Abstract
OBJECTIVES: The purpose of the present study is to estimate autoantibody (auto-AB) frequency, clinical characteristics, and 21-day outcome of participants in the Pediatric Acute Liver Failure Study Group (PALFSG) by antinuclear antibody, smooth muscle antibody, and liver-kidney microsomal (LKM) antibody status. METHODS: Auto-ABs were determined at local and/or central laboratories. Subjects were assigned to autoimmune hepatitis (AIH), indeterminate, and other diagnoses groups. RESULTS: Between 1999 and 2010, 986 subjects were enrolled in the PALFSG. At least 1 auto-AB result was available for 722 (73.2%). At least 1 auto-AB was positive for 202 (28.0%). Diagnoses for auto-AB+ subjects were AIH (63), indeterminate (75), and other (64). Auto-ABs were more common in Wilson disease (12/32, 37.5%) compared with other known diagnoses (52/253, 20.6%, P = 0.03). LKM+ subjects were younger (median 2.4 vs 9.1 years, P < 0.001) and more likely to undergo liver transplantation (53.3% vs 31.4% P = 0.02) than other auto-AB+/LKM- subjects. Steroid treatment of subjects who were auto-AB+ was not significantly associated with survival and the subgroup with known diagnoses other than AIH had a higher risk of death. CONCLUSIONS: Auto-ABs are common in children with acute liver failure, occurring in 28%. Auto-AB+ subjects have similar outcomes to auto-AB negative subjects. LKM+ children are younger and more likely to undergo liver transplantation compared with other auto-AB+ subjects. Although auto-AB may indicate a treatable condition, positivity does not eliminate the need for a complete diagnostic evaluation because auto-ABs are present in other conditions. The significance of auto-AB in pediatric acute liver failure remains uncertain, but LKM+ appears to identify a unique population of children who merit further study.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 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".