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Record W4409360237 · doi:10.1053/j.akdh.2025.01.008

Clinical Trials Targeting Recovery and Postdischarge Care in Dialysis for Acute Kidney Injury

2025· review· en· W4409360237 on OpenAlexafffund
Ian E. McCoy, Samuel A. Silver

Bibliographic record

VenueAdvances in Kidney Disease and Health · 2025
Typereview
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsQueen's University
FundersQueen's UniversityAstraZeneca
KeywordsAcute kidney injuryMedicineDialysisIntensive care medicineClinical trialInternal medicine

Abstract

fetched live from OpenAlex

Receipt of dialysis for acute kidney injury is common and increasing. For patients still actively receiving dialysis, it is possible that the dialysis procedure itself decreases the likelihood of kidney recovery or confounds recognition that recovery has occurred. Accordingly, 2 ongoing trials are testing hypotheses that dialysis prescriptions to minimize dialysis-induced ischemia and/or standardize dialysis discontinuation will increase the likelihood of renal recovery compared to usual care. These will be some of the first clinical trials to focus on hospitalized patients during the recovery phase of their acute illness. Meanwhile, clinical trials in the postdischarge population have found that less than 30% of patients choose to enroll when interventions require in-person nephrology follow-up, suggesting more flexible and pragmatic follow-up pathways are needed. Key considerations for future trials in dialysis for acute kidney injury will include recruiting patients at the right time in their clinical course during the window between acute kidney injury development and recovery or death, as well as providing interventions/follow-up over great distances and in multiple care settings. Testing different care strategies in this rigorous manner may eventually help reduce variation in care across centers and identify evidence-based practices that promote kidney recovery in dialysis for acute kidney injury.

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)0.007
metaresearch head score (Gemma)0.024
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.889
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0070.024
Meta-epidemiology (narrow)0.0010.001
Meta-epidemiology (broad)0.0060.001
Bibliometrics0.0010.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.001
Insufficient payload (model declined to judge)0.0000.000

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 head0.098
GPT teacher head0.552
Teacher spread0.454 · 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
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

Citations3
Published2025
Admission routes2
Has abstractyes

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