MétaCan
Menu
Back to cohort
Record W2411001834 · doi:10.4254/wjh.v7.i29.2896

Recurrence of autoimmune liver diseases after liver transplantation

2015· review· en· W2411001834 on OpenAlexaff
Nabiha Faisal

Bibliographic record

VenueWorld Journal of Hepatology · 2015
Typereview
Languageen
FieldMedicine
TopicLiver Diseases and Immunity
Canadian institutionsUniversity of TorontoUniversity Health NetworkToronto General Hospital
Fundersnot available
KeywordsMedicineAutoimmune hepatitisPrimary sclerosing cholangitisLiver transplantationPrimary biliary cirrhosisEtiologyIncidence (geometry)DiseaseCirrhosisLiver diseaseGastroenterologyTransplantationInternal medicineImmunology

Abstract

fetched live from OpenAlex

Liver transplantation (LT) is the most effective treatment modality for end stage liver disease caused by many etiologies including autoimmune processes. That said, the need for transplantation for autoimmune hepatitis (AIH) and primary biliary cirrhosis (PBC), but not for primary sclerosing cholangitis (PSC), has decreased over the years due to the availability of effective medical treatment. Autoimmune liver diseases have superior transplant outcomes than those of other etiologies. While AIH and PBC can recur after LT, recurrence is of limited clinical significance in most, but not all cases. Recurrent PSC, however, often progresses over years to a stage requiring re-transplantation. The exact incidence and the predisposing factors of disease recurrence remain debated. Better understanding of the pathogenesis and the risk factors of recurrent autoimmune liver diseases is required to develop preventive measures. In this review, we discuss the current knowledge of incidence, diagnosis, risk factors, clinical course, and treatment of recurrent autoimmune liver disease (AIH, PBC, PSC) following LT.

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 imitation

Not 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.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0020.001
Science and technology studies0.0000.000
Scholarly communication0.0010.001
Open science0.0010.000
Research integrity0.0010.001
Insufficient payload (model declined to judge)0.0020.001

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.

Opus teacher head0.050
GPT teacher head0.335
Teacher spread0.285 · 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 source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designNot applicable
Domainnot available
GenreReview

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

Citations37
Published2015
Admission routes1
Has abstractyes

Explore more

Same venueWorld Journal of HepatologySame topicLiver Diseases and ImmunityFrench-language works237,207