Epidemiology and the initial presentation of autoimmune hepatitis in Sweden: A nationwide study
Bibliographic record
Abstract
OBJECTIVE: Autoimmune hepatitis (AIH) is a chronic liver disease, which if untreated can lead to cirrhosis and hepatic failure. The aim of the study was to investigate the incidence, prevalence, diagnostic tradition and clinical initial presentation of AIH. MATERIAL AND METHODS: Analyses were performed in 473 patients identified as having probable or definite AIH. RESULTS: The incidence of AIH was 0.85/100,000 (95% CI 0.69-1.01) inhabitants, which is somewhat lower than reported previously. The point prevalence amounted to 10.7/100,000 (95% CI 8.8-13.1), and 76% of the cases were females. The age-related incidence curve was bimodal but men were found to have only one incidence peak in the late teens, whereas women had a peak after menopause. AIH was presented as a spectrum of clinical settings from detected "en passant" to acute liver failure. Almost 30% of patients already had liver cirrhosis at diagnosis. Autoantibodies indicative of AIH type 1 were found in 79% of cases. Other concomitant autoimmune diseases were frequently found (49%). CONCLUSIONS: The incidence and prevalence figures confirm that AIH is a fairly uncommon disease in the Swedish population. Symptoms at presentation were unspecific, but almost half of the patients were jaundiced, with around 30% having liver cirrhosis. The majority of Swedish AIH patients had AIH type 1.
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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 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".