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Record W2528978880 · doi:10.1186/s13054-016-1489-9

Nomenclature for renal replacement therapy in acute kidney injury: basic principles

2016· review· en· W2528978880 on OpenAlexaff
Mauro Neri, Gianluca Villa, Francesco Garzotto, Sean M. Bagshaw, Rinaldo Bellomo, Jorge Cerdá, Fiorenza Ferrari, Silvia Guggia, Michael Joannidis, John A. Kellum, Jeong Chul Kim, Ravindra L. Mehta, Zaccaria Ricci, Alberto Trevisani, Silvio Marafon, William R. Clark, Jean‐Louis Vincent, Claudio Ronco

Bibliographic record

VenueCritical Care · 2016
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of Alberta
Fundersnot available
KeywordsMedicineRenal replacement therapyAcute kidney injuryIntensive care medicineRenal injuryNomenclatureKidneyInternal medicine

Abstract

fetched live from OpenAlex

This article reports the conclusions of a consensus expert conference on the basic principles and nomenclature of renal replacement therapy (RRT) currently utilized to manage acute kidney injury (AKI). This multidisciplinary consensus conference discusses common definitions, components, techniques, and operations of the machines and platforms used to deliver extracorporeal therapies, utilizing a "machine-centric" rather than a "patient-centric" approach. We provide a detailed description of the performance characteristics of membranes, filters, transmembrane transport of solutes and fluid, flows, and methods of measurement of delivered treatment, focusing on continuous renal replacement therapies (CRRT) which are utilized in the management of critically ill patients with AKI. This is a consensus report on nomenclature harmonization for principles of extracorporeal renal replacement therapies. Devices and operations are classified and defined in detail to serve as guidelines for future use of terminology in papers and research.

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)0.008
metaresearch head score (Gemma)0.009
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 score0.008
Threshold uncertainty score0.042

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0080.009
Meta-epidemiology (narrow)0.0020.000
Meta-epidemiology (broad)0.0030.002
Bibliometrics0.0070.009
Science and technology studies0.0010.005
Scholarly communication0.0040.005
Open science0.0040.003
Research integrity0.0030.005
Insufficient payload (model declined to judge)0.0040.004

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 head0.116
GPT teacher head0.467
Teacher spread0.351 · 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

Citations178
Published2016
Admission routes1
Has abstractyes

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