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Record W4224303131 · doi:10.47391/jpma.3987

Development of Banff Classification from 1991 to 2019 for identifying renal allograft rejection: a narrative review for nephrologists

2022· review· en· W4224303131 on OpenAlexaboutno aff
Murtaza Dhrolia, Aasim Ahmad

Bibliographic record

VenueJournal of the Pakistan Medical Association · 2022
Typereview
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineClassification schemeNarrative reviewNarrativePerspective (graphical)PathologyIntensive care medicineArtificial intelligenceData science

Abstract

fetched live from OpenAlex

Renal pathologists, nephrologists and transplant surgeons held a meeting in 1991 at Banff, Canada, and developed a classification scheme that standardised the international classification of renal allograft biopsies and called it the Banff Classification. Following the first meeting, 15 meetings were held, usually every two years, that revised the classification in the light of new evidence and techniques. The latest printed consensus was after the 2019 meeting in Pittsburgh in the United States of America. Several articles have been published in the last 30 years that have created ambiguities for nephrologists and have made things challenging for the expert pathologists. The current perspective review was planned to make it easy and clear for beginners and for practitioners how the Banff Classification has evolved since its inception.

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)0.007
metaresearch head score (Gemma)0.009
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.864
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.009
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
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 head0.115
GPT teacher head0.428
Teacher spread0.313 · 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 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

Citations1
Published2022
Admission routes1
Has abstractyes

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