Severe acute liver disease in adults: Contemporary role of histopathology
Bibliographic record
Abstract
Liver biopsies have consistently contributed to our understanding of the pathogenesis and aetiologies of acute liver disease. As other diagnostic modalities have been developed and refined, the role of biopsy in the management of patients with acute liver failure (ALF), acute-on-chronic liver failure (ACLF) and acute hepatitis, including acute liver injury (ALI), has changed. Liver biopsy remains particularly valuable when first-line diagnostic algorithms fail to determine aetiology. Despite not being identified as a mandatory diagnostic tool in recent clinical guidelines for the management of ALF or ACLF, many centres continue to undertake biopsies given the relative safety of transjugular biopsy in this setting. Several studies have demonstrated that liver biopsy can provide prognostic information, particularly in the context of so-called indeterminate hepatitis, and is extremely useful in excluding conditions such as metastatic tumours that would preclude transplantation. In addition, its widespread use of percutaneous biopsies in cases of less severe acute liver injury, for example in the establishment of a diagnosis of acute presentation of autoimmune hepatitis or confirmation of a probable or definite drug-induced liver injury (DILI), has meant that many centres have seen a shift in the ratio of specimens they are receiving from patients with chronic to acute liver disease. Histopathologists therefore need to be equipped to deal with these challenging specimens. This overview provides an insight into the contemporary role of biopsies (as well as explant and autopsy material) in diagnosing acute liver disease. It outlines up-to-date clinical definitions of liver injury and considers recent recommendations for the diagnosis of AIH and drug-induced, autoimmune-like hepatitis (DI-AIH).
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 | 0.003 | 0.006 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.003 |
| Insufficient payload (model declined to judge) | 0.002 | 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".