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Record W2210116591 · doi:10.1111/ajt.13661

The Revised (2013) Banff Classification for Antibody-Mediated Rejection of Renal Allografts: Update, Difficulties, and Future Considerations

2015· review· en· W2210116591 on OpenAlexfundno aff
Mark Haas

Bibliographic record

VenueAmerican Journal of Transplantation · 2015
Typereview
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
FundersCanadian Society of Transplantation
KeywordsMedicineBiopsyTransplantationPathologyInternal medicine

Abstract

fetched live from OpenAlex

The Banff 2013 classification (Banff 2013) for antibody-mediated rejection (ABMR) in renal allografts represents the first major revision of the original Banff classification for ABMR that was published in 2003. The main impetus for this revision was the need to include C4d-negative ABMR, although this revised classification contains a number of additional features based on findings reported from 2007 to 2013. Since its publication, several studies have examined the validity of different aspects of Banff 2013 and compared it to earlier (2003, 2007) versions of the Banff ABMR classification. Recent evidence, albeit limited, indicates that Banff 2013 represents an improvement over the previous versions, enhancing our ability to accurately diagnose cases of acute/active and chronic active ABMR on renal allograft biopsy. Molecular studies appear to justify the threshold value of glomerulitis plus peritubular capillaritis score ≥2 required by Banff 2013 for the diagnosis of C4d-negative ABMR; however, other aspects of the classification, including its overall interobserver reproducibility, the clinical significance of the category of C4d staining without evidence of rejection, and whether surrogate markers might potentially substitute for the requirement for the presence of donor-specific antibodies, require additional investigation.

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)0.001
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 score0.981
Threshold uncertainty score0.631

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
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 head0.038
GPT teacher head0.353
Teacher spread0.316 · 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

Citations118
Published2015
Admission routes1
Has abstractyes

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