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Antibody-Mediated Rejection Criteria - an Addition to the Banff ’97 Classification of Renal Allograft Rejection

2003· article· en· W2110079652 on OpenAlexaff
Lorraine C. Racusen, Robert B. Colvin, Kim Solez, Michael J. Mihatsch, Philip F. Halloran, Patricia Campbell, Michael Cecka, Jean‐Pierre Cosyns, Anthony J. Demetris, Michael C. Fishbein, Agnes B. Fogo, Peter Furness, Ian W. Gibson, Denis Glotz, P Häyry, Lawrence Hunsickern, Michael Kashgarian, Ronald H. Kerman, Alex J. Magil, Robert A. Montgomery, Kunio Morozumi, Volker Nickeleit, Parmjeet Randhawa, Heinz Regele, Daniel Serón, Surya V. Seshan, Ståle Sund, Kiril Trpkov

Bibliographic record

VenueAmerican Journal of Transplantation · 2003
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsSt. Paul's HospitalHealth Sciences CentreRockyview General HospitalUniversity of Alberta
FundersNovartis PharmaGenentechNational Institutes of HealthBristol-Myers Squibb
KeywordsMedicineTransplantationPathologicalRenal transplantPopulationIntensive care medicineKidney transplantationPathologySurgery

Abstract

fetched live from OpenAlex

Antibody-mediated rejection (AbAR) is increasingly recognized in the renal allograft population, and successful therapeutic regimens have been developed to prevent and treat AbAR, enabling excellent outcomes even in patients highly sensitized to the donor prior to transplant. It has become critical to develop standardized criteria for the pathological diagnosis of AbAR. This article presents international consensus criteria for and classification of AbAR developed based on discussions held at the Sixth Banff Conference on Allograft Pathology in 2001. This classification represents a working formulation, to be revisited as additional data accumulate in this important area of renal transplantation.

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)0.011
metaresearch head score (Gemma)0.011
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Theoretical or conceptual · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: Methods
Teacher disagreement score0.011
Threshold uncertainty score0.061

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0110.011
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0050.002
Science and technology studies0.0010.002
Scholarly communication0.0020.002
Open science0.0030.002
Research integrity0.0010.003
Insufficient payload (model declined to judge)0.0030.001

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 head0.024
GPT teacher head0.328
Teacher spread0.304 · 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 designTheoretical or conceptual
Domainnot available
GenreMethods

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

Citations1,064
Published2003
Admission routes1
Has abstractyes

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