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Record W3204876619 · doi:10.1097/mcc.0000000000000891

Timing of renal-replacement therapy in intensive care unit-related acute kidney injury

2021· article· en· W3204876619 on OpenAlexaff

Bibliographic record

VenueCurrent Opinion in Critical Care · 2021
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsAlberta Health ServicesUniversity of TorontoUniversity of AlbertaSt. Michael's HospitalUniversity of Calgary
Fundersnot available
KeywordsAcute kidney injuryCritically illIntensive careRenal replacement therapyEarly goal-directed therapyMEDLINE

Abstract

fetched live from OpenAlex

PURPOSE OF REVIEW: The optimal timing of renal-replacement therapy (RRT) initiation for the management of acute kidney injury (AKI) in the intensive care unit (ICU) is frequently controversial. An earlier-strategy has biological rationale, even in the absence of urgent indications; however, a delayed-strategy may prevent selected patients from receiving RRT and avoid complications related to RRT. RECENT FINDINGS: Previous studies assessing the optimal timing of RRT initiation found conflicting results, contributing to variation in clinical practice. The recent multinational trial, standard vs. accelerated initiation of renal replacement therapy in acute kidney injury (STARRT-AKI) found no survival benefit and a higher risk of RRT dependence with an accelerated compared to a standard RRT initiation strategy in critically ill patients with severe AKI. Nearly 40% of patients allocated to the standard-strategy group did not receive RRT. The Artificial Kidney Initiation in Kidney Injury-2 (AKIKI-2) trial further assessed delayed compared to more-delayed strategies for RRT initiation. The more-delayed strategy did not confer an increase in RRT-free days and was associated with a higher risk of death. SUMMARY: Early preemptive initiation of RRT in critically ill patients with AKI does not confer clear clinical benefits. However, protracted delays in RRT initiation may be harmful.

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 distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.003
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: none
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.452
Threshold uncertainty score0.950

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.003
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0010.000

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 head0.189
GPT teacher head0.498
Teacher spread0.310 · 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 teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

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

Citations12
Published2021
Admission routes1
Has abstractyes

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