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Record W2063664799 · doi:10.1093/ndt/gfu371

Better understanding of transplant glomerulopathy secondary to chronic antibody-mediated rejection

2014· review· en· W2063664799 on OpenAlexaff
Ádám Remport, Béla Iványi, Zoltán Máthé, Kathryn Tinckam, István Mucsi, Miklos Z. Molnar

Bibliographic record

VenueNephrology Dialysis Transplantation · 2014
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of TorontoUniversity Health Network
Fundersnot available
KeywordsMedicineRituximabEculizumabPlasmapheresisThrombotic microangiopathyImmunologyAntibodyInternal medicineGastroenterologyComplement system

Abstract

fetched live from OpenAlex

Transplant glomerulopathy (TG) is generally accepted to result from repeated episodes of endothelial activation, injury and repair, leading to pathological abnormalities of double contouring or multi-layering of the glomerular basement membrane. TG is a major sequel of chronic active antibody-mediated rejection (cABMR), from pre-existing or de novo anti-HLA antibodies. Hepatitis C infection, thrombotic microangiopathy or other factors may also contribute to TG development. TG prevalence is 5-20% in most series, reaching 55%, in some high-risk cohorts, and is associated with worse allograft outcomes. Despite its prevalence and clinical significance, few well-studied treatment options have been proposed. Similar to desensitization protocols, plasmapheresis with or without immunoabsorption, high-dose intravenous immunoglobulin, rituximab, bortezomib and eculizumab have been proposed in the treatment of TG due to cABMR individually or in various combinations. Robust clinical trials are urgently needed to address this major cause of allograft loss. This review summarizes the current knowledge of the epidemiology, etiology, pathology, and the preventive and treatment options for TG secondary to cABMR.

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 categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.647
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0030.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0010.001
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 head0.032
GPT teacher head0.325
Teacher spread0.292 · 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.

Study designOther design
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

Citations39
Published2014
Admission routes1
Has abstractyes

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