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Record W3009860442 · doi:10.1016/j.kint.2020.02.010

Nomenclature for kidney function and disease: report of a Kidney Disease: Improving Global Outcomes (KDIGO) Consensus Conference

2020· article· en· W3009860442 on OpenAlexaff
Andrew S. Levey, Kai‐Uwe Eckardt, Nijsje Dorman, Stacy Christiansen, Ewout J. Hoorn, Julie R. Ingelfinger, Lesley A. Inker, Adeera Levin, Rajnish Mehrotra, Paul M. Palevsky, Mark A. Perazella, Allison Tong, Susan J. Allison, Detlef Böckenhauer, Josephine P. Briggs, Jonathan S. Bromberg, Andrew Davenport, Harold I. Feldman, Denis Fouque, Ron T. Gansevoort, John S. Gill, Eddie L. Greene, Brenda R. Hemmelgarn, Matthias Kretzler, Mark Lambie, Pascale H. Lane, Joseph Laycock, Shari E. Leventhal, Michael Mittelman, Patricia Morrissey, Marlies Ostermann, Lesley Rees, Pierre Ronco, Franz Schaefer, Jennifer St. Clair Russell, Caroline Vinck, Stephen B. Walsh, Daniel E. Weiner, Michael Cheung, Michel Jadoul, Wolfgang C. Winkelmayer­

Bibliographic record

VenueKidney International · 2020
Typearticle
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of CalgaryUniversity of British Columbia
FundersNational Institutes of HealthBayer HealthCareAgence Nationale de la RechercheAstraZenecaTufts Medical CenterFresenius Medical Care North AmericaSanofi
KeywordsKidney diseaseMedicineRenal functionKidneyIntensive care medicineGlossaryAcute kidney injuryAlbuminuriaNephrologyDiseaseInternal medicineLinguistics

Abstract

fetched live from OpenAlex

The worldwide burden of kidney disease is rising, but public awareness remains limited, underscoring the need for more effective communication by stakeholders in the kidney health community. Despite this need for clarity, the nomenclature for describing kidney function and disease lacks uniformity. In June 2019, Kidney Disease: Improving Global Outcomes (KDIGO) convened a Consensus Conference with the goal of standardizing and refining the nomenclature used in the English language to describe kidney function and disease, and of developing a glossary that could be used in scientific publications. Guiding principles of the conference were that the revised nomenclature should be patient-centered, precise, and consistent with nomenclature used in the KDIGO guidelines. Conference attendees reached general consensus on the following recommendations: (i) to use "kidney" rather than "renal" or "nephro-" when referring to kidney disease and kidney function; (ii) to use "kidney failure" with appropriate descriptions of presence or absence of symptoms, signs, and treatment, rather than "end-stage kidney disease"; (iii) to use the KDIGO definition and classification of acute kidney diseases and disorders (AKD) and acute kidney injury (AKI), rather than alternative descriptions, to define and classify severity of AKD and AKI; (iv) to use the KDIGO definition and classification of chronic kidney disease (CKD) rather than alternative descriptions to define and classify severity of CKD; and (v) to use specific kidney measures, such as albuminuria or decreased glomerular filtration rate (GFR), rather than "abnormal" or "reduced" kidney function to describe alterations in kidney structure and function. A proposed 5-part glossary contains specific items for which there was general agreement. Conference attendees acknowledged limitations of the recommendations and glossary, but they considered standardization of scientific nomenclature to be essential for improving communication.

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)0.073
metaresearch head score (Gemma)0.077
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: Methods · Consensus signal: none
Teacher disagreement score0.073
Threshold uncertainty score0.385

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0730.077
Meta-epidemiology (narrow)0.0030.001
Meta-epidemiology (broad)0.0050.007
Bibliometrics0.0140.013
Science and technology studies0.0030.004
Scholarly communication0.0090.006
Open science0.0100.009
Research integrity0.0060.017
Insufficient payload (model declined to judge)0.0060.007

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 head0.020
GPT teacher head0.283
Teacher spread0.263 · 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

Citations713
Published2020
Admission routes1
Has abstractyes

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