Autoimmune Pancreatitis in Children: Characteristic Features, Diagnosis, and Management
Bibliographic record
Abstract
OBJECTIVES: Autoimmune pancreatitis (AIP) is an increasingly recognized disease entity, but data in children are limited. AIP presentation and outcome in children might differ from the adult experience. We aim to determine the characteristic features of AIP in children. METHODS: Data about clinical symptoms, imaging, histology, and treatment were collected using two sources: (i) a systematic literature search and (ii) the INSPPIRE database, the largest international multicenter study of pancreatitis in children and the Cliniques Universitaires St-Luc (CUSL) registry. RESULTS: We identified 48 AIP cases: 30 from literature review, 14 from INSPPIRE, and 4 from CUSL. The median age at diagnosis was 13 years (range 2-17 years). Abdominal pain (43/47, 91%) and/or obstructive jaundice (20/47, 42%) were the most common symptoms at diagnosis. Elevated serum IgG4 levels were only observed in 9/40 (22%) children. Cross-sectional imaging studies were abnormal in all children including hypointense global or focal gland enlargement (39/47, 83%), main pancreatic duct irregularity (30/47, 64%), and common bile duct stricture (26/47, 55%). A combination of lymphoplasmacytic inflammation, pancreatic fibrosis, and ductal granulocyte infiltration were the main histological findings (18/25, 72%). Children with AIP had a prompt clinical response to steroids. Complications of AIP included failure of exocrine (4/25, 16%) and endocrine (3/27, 11%) pancreas function. CONCLUSIONS: Pediatric AIP has a distinct presentation with features similar to type 2 AIP in adults. This comprehensive report on the largest group of children with AIP to date is expected to help with the diagnosis and management of this disease and pave the way for future research studies.
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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 | 0.003 | 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".