Letter: reversibility of hepatic fibrosis and immunosuppression withdrawal in patients with autoimmune hepatitis/primary biliary cirrhosis overlap syndrome – author's reply
Bibliographic record
Abstract
The experience of Drs Yilmaz and Dayanan1 in patients with features of autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC) is similar to that in patients with classical AIH.2, 3 Liver inflammation is probably the principal driver of fibrosis in each of these clinical syndromes.4 As Drs Yilmaz and Dayanan suggest, the suppression of inflammation can be an effective anti-fibrotic strategy even when the inflammation is accompanied by features of PBC. Other studies of PBC support these observations.5, 6 The inflammatory features associated with AIH lack disease-specificity, and their occurrence in PBC has identified patients whose outcomes may be improved by management strategies that combine corticosteroids (with or without azathioprine) and ursodeoxycholic acid.7, 8 The experience of Drs Yilmaz and Dayanan suggests that immunosuppressive therapy alone may be effective as an anti-fibrotic regimen in some patients. Furthermore, outcomes may be better than in those with classical AIH or PBC. The overlap syndromes are clinical descriptions based largely on clinical judgments, and the AIH/PBC overlap syndrome described by Drs Yilmaz and Dayanan exists within a clinical spectrum that is bounded by patients with mainly AIH and weak manifestations of PBC and by patients with mainly PBC and inflammatory features reminiscent of AIH.8 Immunosuppressive therapy has a role across the spectrum, but outcomes may differ depending on the predominant component.8 The absence of relapse after drug withdrawal suggests that PBC may have been the predominant disease in this experience. The observations of Drs Yilmaz and Dayanan indicate that immunosuppressive therapy can have anti-fibrotic effects in immune-mediated liver diseases outside the classical phenotype of AIH. Future objectives must be to characterise those patients who are likely to respond, validate non-invasive methods to monitor fibrosis, and rigorously evaluate site-specific anti-fibrotic interventions that might supplement or replace current strategies.9 The author's declarations of personal and financial interests are unchanged from those in the original article.9
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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 | 0.002 | 0.019 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.001 | 0.002 |
| Scholarly communication | 0.001 | 0.003 |
| Open science | 0.003 | 0.001 |
| Research integrity | 0.022 | 0.016 |
| Insufficient payload (model declined to judge) | 0.004 | 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".