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Record W4411721626 · doi:10.1093/ehjacc/zuaf084

Severe acute kidney injury in the intensive care unit: step-to-step management

2025· article· en· W4411721626 on OpenAlexaff
Mauro Riccardi, Matteo Pagnesi, Carlo Lombardi, Marco Metra

Bibliographic record

VenueEuropean Heart Journal Acute Cardiovascular Care · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsSurgical Specialties (Canada)
Fundersnot available
KeywordsMedicineAcute kidney injuryCardiogenic shockRenal replacement therapyIntensive care unitDialysisIntensive care medicineCreatinineRenal functionIntensive careHeart failureInternal medicineMyocardial infarction

Abstract

fetched live from OpenAlex

Acute kidney injury (AKI) is a sudden loss of renal function limited to 7 days with increased basal serum creatinine levels and/or decreased urinary production. AKI is a frequent condition in the intensive care unit (ICU) ranging from 13% to 36% in patients hospitalized with acute heart failure, up to 80% in patients with cardiogenic shock (CS). AKI requiring dialysis is also common (5% to 8%) and can exceed 13% in patients with CS. AKI is consistently associated with increased mortality in both the short-term, especially when dialysis is needed, and the long-term. The aim of this review is to provide an update on step-by-step management, from pharmacological treatment to renal replacement therapy, in patients with severe AKI in ICU patients with fluid overload.

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)0.002
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMeta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: none
Teacher disagreement score0.557
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0020.000
Meta-epidemiology (narrow)0.0010.000
Meta-epidemiology (broad)0.0010.002
Bibliometrics0.0010.002
Science and technology studies0.0010.000
Scholarly communication0.0000.000
Open science0.0010.001
Research integrity0.0000.002
Insufficient payload (model declined to judge)0.0000.001

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 head0.027
GPT teacher head0.327
Teacher spread0.299 · 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.

Study designNot applicable
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

Citations4
Published2025
Admission routes1
Has abstractyes

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