Autoimmune hepatitis in the Alaska Native population: autoantibody profile and <scp>HLA</scp> associations
Bibliographic record
Abstract
BACKGROUND & AIMS: The Alaska Native population is one of few populations in the world with a high prevalence of autoimmune hepatitis. The objective of this study was to determine the frequency and HLA and clinical associations of autoantibodies in Alaska Native people with autoimmune hepatitis. METHODS: Alaska Native individuals with autoimmune hepatitis were recruited in clinics conducted statewide. Sera were tested for the presence of autoantibodies described in either autoimmune hepatitis or rheumatic disease. Associations between autoantibodies and HLA alleles and clinical features were assessed. RESULTS: Seventy-one patients were included. At the study visit, 34 patients (47.9%) had antibodies to double-stranded DNA by immunofluorescence; 27 (38.0%) had anti-neutrophil cytoplasmic antibodies; and 11 (15.5%) had anti-Ro antibodies. Only one person had antibodies against soluble liver antigen, and in that person, anti-Ro was absent. Associations were found between autoantibodies and HLA alleles, including positive associations between HLA DR3 and anti-double-stranded DNA antibodies and between HLA DR14 and antineutrophil cytoplasmic antibodies. There was no association between autoantibodies and clinical outcomes. CONCLUSIONS: As in other populations, the prevalence of anti-double-stranded DNA antibodies and antineutrophil cytoplasmic antibodies is high in Alaska Native people with autoimmune hepatitis. In contrast to data from other populations, there is a lower prevalence of anti-soluble liver antigen and a lack of association between anti-Ro and anti-soluble liver antigen. In addition, the HLA profile and associations with autoantibodies are unique. No clear prognostic implications of autoantibodies have emerged in this population.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| 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 teacher head, 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".