Autoimmune liver disease and concomitant extrahepatic autoimmune disease
Bibliographic record
Abstract
AIM: To assess the frequency and clinical impact of associated extrahepatic autoimmune diseases (EAD) on autoimmune liver diseases (ALD). PATIENTS AND METHODS: We investigated 608 patients with ALD (327 autoimmune hepatitis - AIH and 281 primary biliary cirrhosis - PBC) for concomitant EAD. RESULTS: In both AIH and PBC, we observed a high prevalence of EAD (29.9 and 42.3%, respectively); both diseases showed a significant association with autoimmune thyroid disease, followed by autoimmune skin disease, celiac disease, and vasculitis in AIH patients and sicca syndrome, CREST syndrome, and celiac disease in PBC patients. At diagnosis, AIH patients with concurrent EAD were more often asymptomatic than patients with isolated AIH (P<0.01). CONCLUSION: Our study confirms the strict association between ALD and EAD, in particular with autoimmune thyroid disease. In the light of our results, all patients with an EAD should be assessed for the concomitant presence of an asymptomatic ALD.
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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 | 0.002 |
| 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) | 0.003 | 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".