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Banff 07 Classification of Renal Allograft Pathology: Updates and Future Directions

2008· article· en· W2098773501 on OpenAlexafffund
Kim Solez, Robert B. Colvin, Lorraine C. Racusen, Mark Haas, B. Sis, Michael Mengel, Philip F. Halloran, William M. Baldwin, G. Bánfi, A. Bernard Collins, Borja G. Cosío, Daísa Silva Ribeiro David, Cinthia B. Drachenberg, Gunilla Einecke, Agnes B. Fogo, Ian W. Gibson, Denis Glotz, Samy S. Iskandar, Edward S. Kraus, Evelyne Lerut, Roslyn B. Mannon, Michael J. Mihatsch, Brian J. Nankivell, Volker Nickeleit, John C. Papadimitriou, Parmjeet Randhawa, Heinz Regele, Karine Renaudin, Ian S.D. Roberts, Daniel Serón, R. Neal Smith, Marialuisa Valente

Bibliographic record

VenueAmerican Journal of Transplantation · 2008
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsUniversity of ManitobaThe Metabolomics Innovation CentreUniversity of Alberta
FundersAstellas PharmaUniversity of Alberta
KeywordsMedicineGrading (engineering)TransplantationBiopsyPathologyRenal transplantSurgery

Abstract

fetched live from OpenAlex
No abstract in any covered source. Its absence is recorded, not treated as a negative.

No abstract. This is not a gap in this database; OpenAlex has none either. 23.3% of the frame is in this state, and the screen finds HALF as much metaresearch here, so the absence is a measured bias rather than a missing field.

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)0.010
metaresearch head score (Gemma)0.016
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Methods · Consensus signal: none
Teacher disagreement score0.015
Threshold uncertainty score0.055

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0100.016
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0080.005
Science and technology studies0.0010.003
Scholarly communication0.0030.004
Open science0.0040.002
Research integrity0.0020.003
Insufficient payload (model declined to judge)0.0060.004

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 head0.015
GPT teacher head0.278
Teacher spread0.262 · 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 designNot applicable
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

Citations1,856
Published2008
Admission routes2
Has abstractno

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