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Record W4295760637 · doi:10.2215/cjn.05450522

Indications for and Timing of Initiation of KRT

2022· review· en· W4295760637 on OpenAlexaff
Marlies Ostermann, Sean M. Bagshaw, Nuttha Lumlertgul, Ron Wald

Bibliographic record

VenueClinical Journal of the American Society of Nephrology · 2022
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of TorontoSt. Michael's HospitalUniversity of AlbertaAlberta Health Services
FundersNational Institute for Health and Care Research
KeywordsMedicineIntensive care medicine

Abstract

fetched live from OpenAlex

KRT is considered for patients with severe AKI and associated complications. The exact indications for initiating KRT have been debated for decades. There is a general consensus that KRT should be considered in patients with AKI and medically refractory complications ("urgent indications"). "Relative indications" are more common but defined with less precision. In this review, we summarize the latest evidence from recent landmark clinical trials, discuss strategies to anticipate the need for KRT in individual patients, and propose an algorithm for decision making. We emphasize that the decision to consider KRT should be made in conjunction with other forms of organ support therapies and important nonkidney factors, including the patient's preferences and overall goals of care. We also suggest future research to differentiate patients who benefit from timely initiation of KRT from those with imminent recovery of kidney function. Until then, efforts are needed to optimize the initiation and delivery of KRT in routine clinical practice, to minimize nonessential variation, and to ensure that patients with persistent AKI or progressive organ failure affected by AKI receive KRT in a timely manner.

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)0.002
metaresearch head score (Gemma)0.005
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 score0.003
Threshold uncertainty score0.009

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0020.005
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0020.001
Bibliometrics0.0020.002
Science and technology studies0.0000.001
Scholarly communication0.0010.001
Open science0.0010.001
Research integrity0.0010.002
Insufficient payload (model declined to judge)0.0030.001

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 head0.295
GPT teacher head0.531
Teacher spread0.236 · 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

Citations33
Published2022
Admission routes1
Has abstractyes

Explore more

Same venueClinical Journal of the American Society of NephrologySame topicAcute Kidney Injury ResearchFrench-language works237,207