MétaCan
Menu
Back to cohort
Record W2082588854 · doi:10.4103/0971-4065.62086

The evolution of the Banff classification schema for diagnosing renal allograft rejection and its implications for clinicians

2010· article· en· W2082588854 on OpenAlexaboutno aff
Dipankar Bhowmik, A.K. Dinda, P Mahanta, SK Agarwal

Bibliographic record

VenueIndian Journal of Nephrology · 2010
Typearticle
Languageen
FieldMedicine
TopicRenal Transplantation Outcomes and Treatments
Canadian institutionsnot available
Fundersnot available
KeywordsMedicineSchema (genetic algorithms)Renal transplantIntensive care medicineTransplantationInternal medicineInformation retrievalComputer science

Abstract

fetched live from OpenAlex

Till the early 1990s there was no standardized international classification of renal allograft biopsies resulting in considerable heterogeneity in reporting among the various centers. A group of dedicated renal pathologists, nephrologists, and transplant surgeons developed a schema in Banff, Canada in 1991. Subsequently there have been updates at regular intervals. The following review presents the evolution of the Banff classification and its utility for clinicians.

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)0.039
metaresearch head score (Gemma)0.051
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: Review · Consensus signal: Review
Teacher disagreement score0.039
Threshold uncertainty score0.208

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0390.051
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0080.004
Science and technology studies0.0010.003
Scholarly communication0.0030.005
Open science0.0030.001
Research integrity0.0020.005
Insufficient payload (model declined to judge)0.0010.001

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 head0.034
GPT teacher head0.340
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 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
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

Citations47
Published2010
Admission routes1
Has abstractyes

Explore more

Same venueIndian Journal of NephrologySame topicRenal Transplantation Outcomes and TreatmentsFrench-language works237,207