Histopathologic Features, Immunophenotyping, Clonality, and Eubacterial Fluorescence In Situ Hybridization in Cats With Lymphocytic Cholangitis/Cholangiohepatitis
Bibliographic record
Abstract
Feline lymphocytic cholangitis is a poorly characterized disease complex with respect to histologic lesions, immunophenotype, and etiopathogenesis. Seventy-eight cases of feline lymphocytic cholangitis (n = 51) and feline hepatic lymphoma (n = 27) were reviewed using standardized histopathology, immunophenotyping (B cell and T cell), polymerase chain reaction for T-cell receptor (TCR) gene rearrangement, and fluorescence in situ hybridization (FISH) for eubacteria. Five histopathologic features in cases of lymphocytic cholangitis assisted in its differentiation from hepatic lymphoma: bile duct targeting (n = 32, 62.7%), ductopenia (n = 9, 17.6%), peribiliary fibrosis (n = 37, 72.5%), portal B-cell aggregates (n = 36, 70.6%), and portal lipogranulomas (n = 38, 74.5%). The majority of lymphocytic cholangitis cases (n = 35, 68.6%) were T cell predominant; 15 (29.4%) had an equal mix of B cells and T cells, and 1 (1.9%) had a B cell-predominant infiltrate; 66.6% of hepatic lymphoma cases were T-cell lymphomas. TCR clonality results were unexpected, with 17.1% of cases of lymphocytic cholangitis having clonal or oligoclonal populations and with T-cell lymphomas having variable TCR clonality (63.6% clonal or oligoclonal, 36.3% polyclonal). The majority of lymphocytic cholangitis (n = 32 of 36, 88.8%) and all hepatic lymphoma cases had no detectable eubacteria using FISH. As demonstrated here, bile duct targeting, ductopenia, peribiliary fibrosis, portal B-cell aggregates, and portal lipogranulomas are lymphocytic cholangitis features that, along with polyclonal TCR (83%), help differentiate it from hepatic lymphoma. No strong evidence was found implicating in situ bacterial colonization as an etiopathogenesis of lymphocytic cholangitis.
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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 | 0.002 | 0.000 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.001 | 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".