Recent advances in clinical practice: epidemiology of autoimmune liver diseases
Bibliographic record
Abstract
Autoimmune liver diseases are chronic inflammatory hepatobiliary disorders that when classically defined encompass three distinctive clinical presentations; primary biliary cholangitis (PBC), primary sclerosing cholangitis (PSC) and autoimmune hepatitis (AIH). Meaningful changes in disease epidemiology are reported, with increasing incidence and prevalence of AIH and PSC in Europe, and rising prevalence of PBC across Europe, North America and the Asia-Pacific region. However, there appears to be very significant global variation with contemporary incidence rates of disease per 100 000 ranging from 0.84 to 2.75 for PBC, 0.1 to 4.39 for PSC and 0.4 to 2.39 for AIH. Prevalence corresponds, and per 100 000 estimates for PBC range from 1.91 to 40.2, for PSC between 0.78 and 31.7 and for AIH from 4.8 to 42.9. Population-based studies and multicentre observational cohort series provide improved understanding of the clinical course that patients experience, highlighting variations in presenting phenotypes geographically and temporally. Collectively, while autoimmune liver diseases are rare, the clinical burden is disproportionately high relative to population incidence and prevalence. Age, sex and race also impact clinical outcomes, and patient morbidity and mortality are reflected by high need for gastroenterology, hepatology and organ transplant services.
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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 | 0.002 | 0.007 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.006 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 0.002 |
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".