Incidence and prevalence of autoimmune hepatitis in the Ueda area, <scp>Japan</scp>
Bibliographic record
Abstract
AIM: Although autoimmune hepatitis (AIH) is considered to be rare in Japan, precise data on the incidence and prevalence of this disease are scarce due to the lack of a nationwide registry. We therefore conducted a study of these factors over a secondary medical care area. METHODS: We retrospectively investigated the medical records of AIH patients seen during 2004-2009 and prospectively recruited subjects from 2010 to 2014 at our hospital. We surveyed via written questionnaires to all family doctors and hospitals in our secondary medical care area of Ueda, with a population 187 205 individuals over 14 years of age. We also surveyed several core liver disease hospitals in the areas neighboring Ueda. RESULTS: Forty-eight patients with AIH were diagnosed between 2004 and 2014. AIH with histological features of acute hepatitis was increased. The average annual incidence of AIH in the area was 2.23 (age-standardized to the Japanese population). Forty-eight patients (37 patients diagnosed between 2004 and 2014, and 11 patients before 2003) were followed to the study end-point. The prevalence was 23.4 (age-standardized to the Japanese population) on 31 December 2014. After age-standardization to the World Health Organization world standard population, the incidence and prevalence of AIH decreased to 1.52 and 15.0, respectively, likely due to the high proportion of elderly patients in Japan. CONCLUSION: The incidence and prevalence of AIH in Japan may be higher than previously believed due to increased awareness among family doctors, and a rise in the diagnosis of mild or atypical AIH.
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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".