Variants of autoimmune liver diseases: how to diagnose? how to treat?
Bibliographic record
Abstract
Variants of autoimmune liver diseases: diagnosis and treatment 1 2 different AILDs can be diagnosed, a phenomenon that has been initially called an overlap syndrome.In 2011, the International Autoimmune Hepatitis Group stated that even if a patient presents with the features of 2 diseases, for example, AIH and PSC, the final diagnosis should be made based on the predominant features and called the variant of the leading disease.15 A proper diagnosis of an AILD variant is challenging.In contrast to the widely accepted diagnostic criteria for AIH, PBC, and PSC, there is still a lack of well -defined, validated, and internationally agreed upon criteria for these variants.In fact, a large proportion of patients with the features of an overlap syndrome can be easily diagnosed with 2 different AILDs, usually AIH with PSC or PBC, by using current diagnostic criteria for each disease separately.This fact creates a treatment dilemma, as the diagnosis of AIH usually requires starting immunosuppression therapy, 4 whereas these drugs are not recommended for classic PSC and PBC.As mentioned above, the leading component of the disease variant should define its first -line therapy.16 Consequently, there is no clear consensus on how to Introduction Autoimmune liver diseases (AILDs), such as autoimmune hepatitis (AIH), primary sclerosing cholangitis (PSC), and primary biliary cholangitis (PBC), are classified as rare diseases; however, their incidence is growing.These chronic inflammatory liver diseases are associated with a plethora of physical and mental complaints, even at their early stages, 1-3 and may lead to progressive liver fibrosis and eventually liver cirrhosis.4-6 The classic AILDs have all been well -defined in American and European clinical guidelines 4-9 in terms of the type of liver injury, the prevalence of autoantibodies, histological findings, and changes in bile duct imaging studies (TAbLE 1).However, these heterogenic diseases tend to share several common features, and thus, each entity may exhibit symptoms, histological patterns, or the presence of antibodies that are typical of other AILDs (TAbLE 2).Additionally, there are genetic risk factors that are common to all AILDs, such as SH2B3, 10 -13 a non -HLA susceptibility marker that was reported to increase the risk of developing AIH, PSC, PBC, and other autoimmune diseases, for example, type 1 diabetes mellitus.
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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) | 0.002 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".