Extrahepatic Autoimmune Diseases in Patients with Autoimmune Liver Diseases: A Phenomenon Neglected by Gastroenterologists
Bibliographic record
Abstract
Autoimmune liver diseases (AILDs) often coexist with other extrahepatic autoimmune diseases (EHAIDs). The spectrum of EHAIDs in patients with AILDs is similar, whereas the incidence is different. Notably, autoimmune thyroid disease and Sjogren's syndrome are the most common EHAIDs. Associated extrahepatic diseases may predate the appearance of AILDs or coincide with their onset. More frequently, they may appear during the course and even occur years after the diagnosis of AILDs. Importantly, associated EHAIDs may influence the natural course and prognosis of AILDs. To date, a definite pathophysiological pathway which contributes to the coexistence of AILDs and EHAIDs is still lacking. The current view of autoimmunity clustering involves a common susceptibility genetic background which applies to related pathologies. Herein, we review the current published researches regarding EHAIDs in patients with AILDs, particularly in relation to their clinical impact and pathophysiology. In managing patients with AILDs, gastroenterologists should be aware of the possibly associated EHAIDs to ensure a prompt diagnosis and better outcome.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".