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Record W4388537448 · doi:10.1097/tp.0000000000004822

Chronic Active Antibody-mediated Rejection: Opportunity to Determine the Role of Interleukin-6 Blockade

2023· review· en· W4388537448 on OpenAlexaff
Mel Berger, Mary Baliker, Teun van Gelder, Georg A. Böhmig, Roslyn B. Mannon, Deepali Kumar, Steven J. Chadban, Peter Nickerson, Laurie A. Lee, Arjang Djamali

Bibliographic record

VenueTransplantation · 2023
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of ManitobaCanadian Transplant Association
FundersCedars-Sinai Medical CenterCSL Behring
KeywordsMedicineBlockadeClinical trialTransplantationKidney transplantationImmunologyMonoclonal antibodySurrogate endpointAntibodyIntensive care medicineRenal functionInternal medicineReceptor

Abstract

fetched live from OpenAlex

Chronic active antibody-mediated rejection (caAMR) is arguably the most important cause of late kidney allograft failure. However, there are no US Food and Drug Administration (FDA)-approved treatments for acute or chronic AMR and there is no consensus on effective treatment. Many trials in transplantation have failed because of slow and/or inadequate enrollment, and no new agent has been approved by the FDA for transplantation in over a decade. Several lines of evidence suggest that interleukin-6 is an important driver of AMR, and clazakizumab, a humanized monoclonal antibody that neutralizes interleukin-6, has shown promising results in phase 2 studies. The IMAGINE trial (Interleukin-6 Blockade Modifying Antibody-mediated Graft Injury and Estimated Glomerular Filtration Rate Decline) (NCT03744910) is the first to be considered by the FDA using a reasonably likely surrogate endpoint (slope of estimated glomerular filtration rate decline >1 y) for accelerated approval and is the only ongoing clinical trial for the treatment of chronic rejection. This trial offers us the opportunity to advance the care for our patients in need, and this article is a call to action for all transplant providers caring for patients with caAMR.

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: Other design · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.958
Threshold uncertainty score0.984

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
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 head0.073
GPT teacher head0.380
Teacher spread0.306 · 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 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

Citations21
Published2023
Admission routes1
Has abstractyes

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