“Plasma Cell Hepatitis” in Liver Allografts: Identification and Characterization of an IgG4-Rich Cohort
Bibliographic record
Abstract
Plasma cell hepatitis (PCH), also known as "de novo autoimmune" hepatitis, is an increasingly recognized, but suboptimally named and poorly understood, category of late allograft dysfunction strongly resembling autoimmune hepatitis (AIH): They share plasma-cell-rich necro-inflammatory activity on biopsy, autoantibodies and steroid responsiveness, but overlap with rejection is problematic. A retrospective study of clinical, serological, histopathological and IgG4 immunohistological features of PCH (n = 20) in liver allograft recipients, native liver AIH (n = 19) and plasma-cell-rich renal allograft rejection (n = 20) showed: (1) high frequency (44%) of HLA-DR15; (2) less female predominance (p = 0.03) and (3) n = 9/20 PCH recipients showed >25 IgG4+ plasma cells/high-power field (IgG4+ PCH) versus AIH (n = 1/19, p = 0.008) or plasma-cell-rich kidney rejection (n = 2/20, p = 0.03). The IgG4+ PCH (n = 9) subgroup showed lower alanine transaminase (ALT) (p < 0.01) and aspartate transaminase (AST) (p < 0.05) at index biopsy but (a) higher plasma cell number/percentage, (b) more aggressive-appearing portal/periportal and perivenular necro-inflammatory activity and (c) more severe portal/periportal fibrosis than IgG4- PCH (n = 11). Significant demographic, histopathologic and plasma cell phenotype differences between PCH and AIH suggest distinct pathogenic mechanisms for at least the IgG4+ PCH subgroup likely representing an overlap between allo- and auto-immunity. IgG4+ PCH was associated with fibrosis, but also highly responsive to increased immunosuppression.
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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".