Autoimmune liver disease for the non-specialist
Bibliographic record
Abstract
#### Summary points Autoimmune hepatitis, primary biliary cirrhosis, and sclerosing cholangitis represent perhaps 5% of all liver diseases, although no registries exist to estimate the true prevalence. They are presumed autoimmune conditions, usually considered as a diagnosis after viral, metabolic, and drug induced liver injuries have been excluded. The combination of medical and surgical treatments means that, if appropriately diagnosed and managed, these diseases overall have an excellent prognosis. We review these conditions for non-specialists and refer to available guidelines and clinical trial data.1 2 3 Autoimmune liver diseases are chronic, slowly progressive, inflammatory liver diseases that may have overlapping features.4 (fig 1⇓, table 1⇓). Fig 1 Characteristic histological and radiological features of autoimmune liver diseases View this table: Table 1 Summary features of autoimmune liver disease ### Autoimmune hepatitis Autoimmune hepatitis is a relapsing idiopathic hepatitis, encountered more often in women than men across all ages and ethnicities. Ascertainment biases limit available epidemiology; a Swedish study put the annual incidence as 8.5 per 1 000 000 population and point prevalence as 107 per 1 000 000.5 Patients present clinically with arthralgias and fatigue if symptomatic, and a third of patients present with cirrhosis.w1 Raised liver enzymes (transaminases) characterise initial laboratory abnormalities. ### Primary biliary cirrhosis Primary biliary cirrhosis is a slowly progressive, chronic cholestatic disease affecting primarily middle aged women (female:male ratio 9 to 1) and is characterised by …
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.192 | 0.092 |
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".