Autoimmune diseases of the liver, autoimmune hepatitis and primary biliary cirrhosis: Unfinished business
Bibliographic record
Abstract
Autoimmune liver diseases (AILD) including autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC) have attracted much attention since their discovery 50 years ago, but there remain items of unfinished business. These relate to disease susceptibility including genetic influences (HLA and non-HLA genes, genes associated with female predisposition, and others) and environmental influences (infections, chemicals, xenobiotics and medications). Also needed is better characterization of autoantigenic molecules, particularly the anti-F-actin specificity characteristic of AIH, shown here to have functional effects in vitro. Deeper analysis of T-lymphocyte function in AILD should reveal relative contributions of eachof the multiple subsets of T cells now being defined in studies on laboratory animals, CD4(+), CD8(+), Th1, Th2, Th17, memory subsets and regulatory subsets. Diagnostic immunology providers now offer high-performance assay formats that call for systematic clinical assessments to achieve standardization, calibration and optimal information.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.013 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.001 | 0.004 |
| Scholarly communication | 0.005 | 0.010 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.003 | 0.004 |
| Insufficient payload (model declined to judge) | 0.020 | 0.006 |
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".