Liver allograft pathology: approach to interpretation of needle biopsies with clinicopathological correlation
Bibliographic record
Abstract
The spectrum of diseases encountered in post-transplant liver pathology biopsies is broad. In this review, these have been divided as belonging to one of three categories: (1) new-onset/de novo post-transplant abnormalities (early and late), (2) rejection, and (3) recurrence of original disease. The clinical and pathological features of the entities making up each category, with the relevant differential diagnosis and overlaps between and within these groups, are discussed and illustrated. Recurrent or de novo neoplasms make up a fourth category not included in this review. Early new-onset conditions are mostly related to surgical complications, donor factors and ischaemia to the graft. These include reperfusion/preservation injury, lipopeliosis, small-for-size-syndrome, biliary sludge syndrome and hepatic artery thrombosis. The various forms of rejection (cellular, chronic, antibody-mediated, and late atypical rejection) are detailed. Most chronic liver diseases can and do recur in the graft. They may display features that overlap with de novo conditions (eg, primary sclerosing cholangitis versus chronic rejection). As with most cases of allograft biopsy interpretation, accurate diagnosis rests with careful correlation of histological features with clinical, imaging and laboratory findings, and often comparison with previous sequential and follow-up biopsies. Late-onset new diseases include biliary strictures, idiopathic chronic hepatitis and de novo autoimmune hepatitis, among others. This review provides a practical approach to the interpretation of these challenging biopsies. Selected difficult scenarios or conundrums are identified and discussed in the relevant sections.
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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 | 0.009 | 0.011 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.008 | 0.002 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.003 | 0.004 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 0.003 |
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".