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Record W2131206061 · doi:10.1016/s1665-2681(19)31258-x

Antimitochondrial antibody serocoversion post-liver transplant during hepatitis C treatment with peginterferon α, ribavirin and telaprevir

2014· article· en· W2131206061 on OpenAlexaff
Vladimir Marquez-Azalgara, Trana Hussaini, Siegfried R. Erb, Eric M. Yoshida

Bibliographic record

VenueAnnals of Hepatology · 2014
Typearticle
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of British Columbia
Fundersnot available
KeywordsMedicineTelaprevirRibavirinGastroenterologyAntibodyHepatitis CInternal medicineVirologyAntiviral treatmentHepatitis C virusChronic hepatitisImmunologyVirus

Abstract

fetched live from OpenAlex

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.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.036
Threshold uncertainty score0.526

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

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.

Opus teacher head0.020
GPT teacher head0.282
Teacher spread0.262 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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".

Quick stats

Citations2
Published2014
Admission routes1
Has abstractyes

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