A national retrospective study of paediatric end‐stage liver disease as a predictor of change to second‐line therapy in children with autoimmune hepatitis
Bibliographic record
Abstract
BACKGROUND & AIMS: Adult studies of autoimmune hepatitis (AIH) have shown that the model of end-stage liver disease is associated with resistance to first-line treatment. Using a multicentre retrospective database, we sought to determine if the paediatric end-stage liver disease (PELD) score would similarly predict treatment resistance in paediatric AIH. METHODS: One hundred and seventy-one children from 13 Canadian centres who fulfilled the International Autoimmune Hepatitis Group (IAIHG) criteria were included and assessed for change to second-line therapy within 24 months of primary treatment onset. Those with PSC overlap at presentation, or missing data on the PELD variables were excluded. PELD was calculated for all remaining patients. Univariate analysis and receiver-operator characteristic (ROC) curves were performed to determine the predictive ability of the PELD score to change to second-line therapy. RESULTS: A total of 103 children were included with median age of 11 years (range 2-17). Mean PELD was -2.51±8.58. Second-line therapy was used within 24 months of diagnosis in 13 patients. Univariate analysis revealed that change to second-line therapy was associated with higher PELD (P=.028) and internal normalized ratio (INR) (P=.011). ROC curves for PELD and its individual components were performed. The strength of association was strongest with INR (AUC 0.72; CI: 0.58-0.86) although the composite PELD score also showed some predictive ability (AUC 0.67; CI: 0.52-0.81). CONCLUSION: In this paediatric AIH cohort, higher PELD at presentation predicted change to second-line therapy within the first 2 years of follow-up. INR appeared to be the main contributor to that association.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 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".