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Record W4413055879 · doi:10.34067/kid.0000000951

Proceedings of the University of Alabama at Birmingham Continuous Renal Replacement Therapy Academy (2023–2024)

2025· article· en· W4413055879 on OpenAlexaff
William Beaubien‐Souligny, Melissa L. Thompson Bastin, J. Pedro Teixeira, Jorge Cerdá, Michael Connor, Amanda Dijanic Zeidman, Pranav S. Garimella, Luis A. Juncos, Arnaldo Lopez‐Ruiz, Ravindra L. Mehta, Lilia Rizo‐Topete, Samuel A. Silver, J. Ricardo Da Silva, Rajesh Speer, Anitha Vijayan, Catherine Wells, Keith Wille, Lenar Yessayan, Ashita Tolwani, Javier A. Neyra

Bibliographic record

VenueKidney360 · 2025
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsQueen's UniversityCentre Hospitalier de l’Université de Montréal
FundersNational Institute of Diabetes and Digestive and Kidney Diseases
KeywordsRenal replacement therapyIntensive care medicineMedicineAcute kidney injuryIntensive care unitOddsCritically illModalitiesDosingAcute careEmergency medicineInternal medicineHealth carePolitical scienceLogistic regression

Abstract

fetched live from OpenAlex

In this second installment of the proceedings of the University of Alabama at Birmingham Continuous Renal Replacement Therapy (CRRT) Academy, we focus on the topic of transitions of care in acute renal replacement therapy (RRT). Although we have accumulated data from thousands of critically ill patients with AKI randomized to different strategies for RRT initiation, no trial data exist to guide de-escalation of RRT in the intensive care unit. However, for survivors of severe AKI whose kidney function does not recovery rapidly enough to allow for liberation directly from CRRT, successful de-escalation of care requires the transition from CRRT to intermittent RRT modalities. These transition periods must be carefully navigated since they can be a source of complications, such as failure to transition or intradialytic hypotension, which are in turn associated with an increased risk of mortality and reduced odds of kidney recovery. In this review, we focus on the critical factors to consider during de-escalation of RRT care, with a focus on modality transition, the role of volume status in guiding the approach to de-escalation of RRT, and the vital importance of careful dosing of drugs, especially antimicrobial agents, during this transitional period.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesInsufficient payload (model declined to judge)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Not applicable · Consensus signal: Not applicable
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.221
Threshold uncertainty score0.999

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.001
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
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 head0.017
GPT teacher head0.287
Teacher spread0.270 · 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

Citations1
Published2025
Admission routes1
Has abstractyes

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