Advanced histology and impaired liver regeneration are associated with disease severity in acute-onset autoimmune hepatitis
Bibliographic record
Abstract
AIMS: Some cases of acute-onset autoimmune hepatitis (AIH) develop into severe or fulminant forms showing massive/submassive hepatic necrosis, and have a poor prognosis. The pathological features of acute-onset AIH remain uncertain. Ductular (intermediate) hepatocytes after massive/submassive necrosis may serve as hepatic progenitor cells, and could be seen as cytokeratin 7 (CK7)-positive hepatocytes in immunohistochemistry. Therefore, the aim was to examine histological features to obtain a better evaluation of acute-onset AIH. METHODS: The histological features of 27 clinically acute-onset AIH patients were examined by immunohistochemistry using CK7. RESULTS: On staining for CK7, intermediate hepatocytes were less commonly present (P < 0.001) and ductular reactions were more commonly present (P < 0.001) in severe/fulminant patients than in non-severe ones. In severe and fulminant patients, intermediate hepatocytes and intralobular progenitor cells were more commonly present (P < 0.005 and P < 0.05, respectively) and ductular reactions were less commonly present (P = 0.007) in recovered patients than in dead ones. Severe patients had more clinically and histologically advanced disease. CONCLUSIONS: Immunohistochemical evaluation using CK7 might be a useful tool for evaluating liver regeneration, and intermediate hepatocytes and progenitor cells might play an important role in liver regeneration after massive and submassive necrosis in acute-onset AIH.
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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 | 0.000 | 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 | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".