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Record W3013074147 · doi:10.1097/tp.0000000000003137

Summary of the Kidney Disease: Improving Global Outcomes (KDIGO) Clinical Practice Guideline on the Evaluation and Management of Candidates for Kidney Transplantation

2020· review· en· W3013074147 on OpenAlexaff
Steven J. Chadban, Curie Ahn, David A. Axelrod, Bethany J. Foster, Bertram L. Kasiske, Vijah Kher, Deepali Kumar, Rainer Oberbauer, Julio Pascual, Helen Pilmore, James R. Rodrigue, Dorry L. Segev, Neil Sheerin, Kathryn Tinckam, Germaine Wong, Ethan M. Balk, Craig E. Gordon, Amy Earley, Valerie Rofeberg, Greg Knoll

Bibliographic record

VenueTransplantation · 2020
Typereview
Languageen
FieldMedicine
TopicRenal and Vascular Pathologies
Canadian institutionsOttawa HospitalUniversity Health NetworkMcGill University Health CentreUniversity of TorontoMontreal Children's Hospital
Fundersnot available
KeywordsMedicineGuidelineIntensive care medicineCandidacyTransplantationKidney diseasePsychosocialKidney transplantationContext (archaeology)DiseaseInternal medicinePathologyPsychiatry

Abstract

fetched live from OpenAlex

OpenAlex records an abstract for this work, but it could not be fetched just now.

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)0.007
metaresearch head score (Gemma)0.035
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: Not applicable
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.032
Threshold uncertainty score0.063

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0070.035
Meta-epidemiology (narrow)0.0020.001
Meta-epidemiology (broad)0.0030.006
Bibliometrics0.0060.005
Science and technology studies0.0010.001
Scholarly communication0.0020.002
Open science0.0040.002
Research integrity0.0040.007
Insufficient payload (model declined to judge)0.0140.009

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 head0.092
GPT teacher head0.437
Teacher spread0.346 · 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

Citations147
Published2020
Admission routes1
Has abstractyes

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