Population‐based epidemiology study of autoimmune hepatitis: A disease of older women?
Bibliographic record
Abstract
BACKGROUND AND AIM: The etiology of autoimmune hepatitis (AIH) is unknown, and limited epidemiological data are available. Our aim was to perform a population based epidemiological study of AIH in Canterbury, New Zealand. METHODS: To calculate point prevalence, all adult and pediatric outpatient clinics and hospital discharge summaries were searched to identify all cases of AIH in the Canterbury region. Incident cases were recruited prospectively in 2008. Demographic and clinical data were extracted from case notes. Both the original revised AIH criteria and the simplified criteria were applied and cases were included in the study if they had definite or probable AIH. RESULTS: When the original revised criteria were used, 138 cases (123 definite and 14 probable AIH), were identified. Prospective incidence in 2008 was 2.0/100,000 (95% confidence interval [CI] 0.8-3.3/100,000). Point prevalence on 31 December 2008 was 24.5/100,000 (95% CI 20.1-28.9). Age-standardized (World Health Organization standard population) incidence and prevalence were 1.7 and 18.9 per 100,000, respectively. Gender-specific prevalence confirmed a female predominance, while ethnicity-specific prevalence showed higher prevalence in Caucasians. 72% of cases presented after 40 years of age and the peak age of presentation was in the sixth decade of life. CONCLUSIONS: This is the first and largest population-based epidemiology study of AIH in a geographically defined region using standardized inclusion criteria. The observed incidence and prevalence rates are among the highest reported. The present study confirms that AIH presents predominantly in older women, with a peak in the sixth decade, contrary to the classical description of the disease.
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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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| 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".