Antimitochondrial antibody serocoversion post-liver transplant during hepatitis C treatment with peginterferon α, ribavirin and telaprevir
Bibliographic record
Abstract
Pegylated interferon alpha (PEG-IFN α), a key component of chronic hepatitis C therapy, has been linked to the development of auto-antibodies and autoimmune disease. We report the first case of antimitochondrial antibody (AMA) seroconversion during PEG-INF α based therapy after liver.1-4 transplantation. A fiftyseven year-old man five months after liver transplantation was initiated on hepatitis C triple therapy with PEG-INF α, ribavirin and telaprevir. He had failed previous PEG-IFN α and ribavirin 12 years pre-transplant and his AMA remained negative pre-transplant. After twelve weeks of antiviral therapy, he developed elevated liver enzyme tests associated with an AMA seroconversion to seropositivity. A liver biopsy failed to show histological evidence of primary biliary cirrhosis or graft rejection. He was initiated on urseodeoxycholic acid with subsequent improvement of his liver enzymes. This case demonstrates that despite adequate immunosuppression, AMA seroconversion may occur post-transplant during interferon-based therapy. As AMA seroconversion did not occur during the pre-transplant PEG-IFN therapy, we speculate that donor allograft antigens in combination with PEG-IFN may have been a factor in the post-transplant seroconversion.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| 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".