Autoimmune hepatitis/primary biliary cirrhosis overlap syndrome and associated extrahepatic autoimmune diseases
Bibliographic record
Abstract
AIM: To assess the prevalence of concurrent extrahepatic autoimmune diseases in patients with autoimmune hepatitis (AIH)/primary biliary cirrhosis (PBC) overlap syndrome and applicability of the 'mosaic of autoimmunity' in these patients. METHODS: The medical data of 71 AIH/PBC overlap patients were evaluated for associated autoimmune diseases. RESULTS: In the study population, 31 (43.6%) patients had extrahepatic autoimmune diseases, including autoimmune thyroid diseases (13 patients, 18.3%), Sjögren syndrome (six patients, 8.4%), celiac disease (three patients, 4.2%), psoriasis (three patients, 4.2%), rheumatoid arthritis (three patients, 4.2%), vitiligo (two patients, 2.8%), and systemic lupus erythematosus (two patients, 2.8%). Autoimmune hemolytic anemia, antiphospholipid syndrome, multiple sclerosis, membranous glomerulonephritis, sarcoidosis, systemic sclerosis, and temporal arteritis were identified in one patient each (1.4%). A total of 181 autoimmune disease diagnoses were found in our patients. Among them, 40 patients (56.4%) had two, 23 (32.3%) had three, and eight (11.3%) had four diagnosed autoimmune diseases. CONCLUSION: A large number of autoimmune diseases were associated with AIH/PBC overlap patients. Therefore, extended screening for existing autoimmune diseases during the routine assessment of these patients is recommended. Our study suggests that the concept of 'mosaic of autoimmunity' is a valid clinical entity that is applicable to patients with AIH/PBC overlap syndrome.
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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 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 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".