Detection of polyreactive immunoglobulin G facilitates diagnosis in children with autoimmune hepatitis
Bibliographic record
Abstract
OBJECTIVE: The detection of autoantibodies is essential to diagnose autoimmune hepatitis (AIH). Particularly in children, specificity of autoantibodies decreases due to lower titers being diagnostic and being present not only in AIH but also in other liver diseases. Recently, quantification of polyreactive IgG (pIgG) for detection of adult AIH showed the highest overall accuracy compared to antinuclear antibodies (ANA), anti-smooth muscle antibodies (anti-SMA), anti-liver kidney microsomal antibodies (anti-LKM) and anti-soluble liver antigen/liver pancreas antibodies (anti-SLA/LP). We aimed to evaluate the diagnostic value of pIgG for pediatric AIH. DESIGN: pIgG, quantified using HIP1R/BSA coated ELISA, and immunofluorescence on rodent tissue sections were performed centrally. The diagnostic fidelity to diagnose AIH was compared to conventional autoantibodies of AIH in training and validation cohorts from a retrospective, European multi-center cohort from nine centers from eight European countries composed of existing biorepositories from expert centers (n = 285). RESULTS: IgG from pediatric AIH patients exhibited increased polyreactivity to multiple protein and non-protein substrates compared to non-AIH liver diseases and healthy children. pIgG had an AUC of 0.900 to distinguish AIH from non-AIH liver diseases. pIgG had a 31-73% higher specificity than ANA and anti-SMA and comparable sensitivity that was 6-20 times higher than of anti-SLA/LP, anti-LC1 and anti-LKM. pIgG had a 21-34% higher accuracy than conventional autoantibodies, was positive in 43-75% of children with AIH and normal IgG and independent from treatment response. CONCLUSION: Detecting pIgG improves the diagnostic evaluation of pediatric AIH compared to conventional autoantibodies, primarily owing to higher accuracy and specificity.
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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 | 0.002 | 0.003 |
| 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 | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".