Incidence and Characteristics of Autoimmune Hepatitis
Bibliographic record
Abstract
BACKGROUND AND OBJECTIVES: Autoimmune hepatitis (AIH) is a progressive inflammatory liver disease of unknown etiology, with limited population-based estimates of pediatric incidence. We reported the incidence of pediatric AIH in Canada and described its clinical characteristics. METHODS: We conducted a retrospective cohort study of patients aged <18 years diagnosed with AIH between 2000-2009 at all pediatric centers in Canada. RESULTS: A total of 159 children with AIH (60.3% female, 13.2% type 2 AIH) were identified. Annual incidence was 0.23 per 100000 children. Median age at presentation for type 1 was 12 years (interquartile range: 11-14) versus 10 years for type 2 (interquartile range: 4.5-13) (P = .03). Fatigue (58%), jaundice (54%), and abdominal pain (49%) were the most common presenting symptoms. Serum albumin (33 vs 38 g/L; P = .03) and platelet count (187 000 vs 249 000; P <.001) were significantly lower and the international normalized ratio (1.4 vs 1.2; P <.001) was higher in cirrhotic versus noncirrhotic patients. Initial treatment included corticosteroids (80%), azathioprine (32%), and/or cyclosporine (13%). Response to treatment at 1 year was complete in 90%, and partial in 3%. 3% of patients had no response, and 3% responded and later relapsed. Nine patients underwent liver transplantation, and 4 patients died at a mean follow-up of 4 years. CONCLUSIONS: AIH is uncommon in children and adolescents in Canada. Type 1 AIH was diagnosed 5.5 times more frequently than type 2 AIH. Most patients respond well to conventional therapy, diminishing the need for liver transplantation.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.000 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".