The prevalence and disease course of autoimmune liver diseases in Greenland
Bibliographic record
Abstract
Autoimmune liver diseases are rare serious diseases causing chronic inflammation and fibrosis in the liver parenchyma and bile ducts. Yet, the prevalence and burden of autoimmune liver diseases are largely unexplored in Arctic native populations. We investigated the prevalence and management of autoimmune liver diseases in Greenland using nationwide cross-sectional register data and subsequent medical chart reviews validating diagnoses and extracting liver histology examinations and medical treatments. The overall prevalence of autoimmune liver diseases in Greenland was 24.6 per 100,000 (95% CI: 14.7-41.3). This was based on 7 patients with autoimmune hepatitis (AIH) (12.3 per 100,000), 3 patients with primary biliary cholangitis (PBC) (5.3 per 100,000), 4 patients with AIH/PBC overlap disease (7.0 per 100,000), and no patients with primary sclerosing cholangitis. All diagnoses were confirmed by liver histology examinations. Medical treatments adhered to internal recommendations and induced complete remission in most patients with AIH, and complete or partial remission in 1 patient with PBC and 3 patients with AIH/PBC overlap disease. One patient had established cirrhosis at the time of diagnosis, while 2 patients progressed to cirrhosis. In conclusion, the prevalence of autoimmune liver diseases was lower in Greenland than in Scandinavia and among Alaska Inuit.
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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 | 0.000 | 0.001 |
| 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 | 0.000 | 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 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".