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Record W4379468102 · doi:10.1016/j.ekir.2023.05.026

Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Disease (WE-ROCK)

2023· article· en· W4379468102 on OpenAlexaff
Shina Menon, Kelli A. Krallman, Ayse Akcan‐Arikan, Dana Y. Fuhrman, Stephen M. Gorga, Theresa Mottes, Nicholas J. Ollberding, Zaccaria Ricci, Natalja L. Stanski, David T. Selewski, Danielle E. Soranno, Michael Zappitelli, Huaiyu Zang, Katja M. Gist, Emily Ahern, Issa Alhamoud, Rashid Alobaidi, Pilar Antón-Martín, Shanthi S. Balani, Matthew Barhight, Abby Basalely, Amee M. Bigelow, Gabriella Bottari, Andrea Cappoli, Eileen Ciccia, Michaela Collins, Denise Colosimo, Gérard Cortina, Mihaela Damian, Sara de la Mata Navazo, Gabrielle DeAbreu, Akash Deep, Kathy L Ding, Kristin Dolan, Sarah N. Fernández, Ben Gelbart, Francesco Guzzi, Isabella Guzzo, Taiki Haga, Elizabeth Harvey, Denise C. Hasson, Taylor Hill-Horowitz, Haleigh Inthavong, Catherine Joseph, Ahmad Kaddourah, Aadil Kakajiwala, Aaron Kessel, S Korn, David M. Kwiatkowski, Jasmine Lee, Laurance Lequier, Tina Madani Kia, Kenneth E. Mah, Eleonora Marinari, Susan D. Martin, Tahagod Mohamed, Catherine Morgan, Melissa Muff‐Luett, Siva P. Namachivayam, Tara M. Neumayr, Jennifer Nhan, Abigail Alexandra O'Rourke, Matthew G. Pinto, Dua Qutob, Valeria Raggi, Stéphanie Reynaud, Zachary A. Rumlow, María José Santiago, Emily See, Carmela Serpe, Alyssa Serratore, Ananya Shah, Weiwen V. Shih, Hyungoo Shin, Cara Slagle, Sonia Solomon, Rachana Srivastava, Michelle C. Starr, Erin K. Stenson, Amy E. Strong, Susan A. Taylor, Sameer Thadani, Amanda M. Uber, Brynna Van Wyk, Tennille N. Webb, Emily E. Zangla

Bibliographic record

VenueKidney International Reports · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsUniversity of AlbertaSickKids FoundationHospital for Sick ChildrenUniversity of Toronto
FundersEunice Kennedy Shriver National Institute of Child Health and Human DevelopmentNational Institute of General Medical SciencesGerber Foundation
KeywordsRenal replacement therapyMedicineAcute kidney injuryNephrologyIntensive care medicineDialysisIntensive care unitKidney diseaseRetrospective cohort studyEmergency medicineInternal medicine

Abstract

fetched live from OpenAlex

Introduction: Continuous renal replacement therapy (CRRT) is used for the symptomatic management of acute kidney injury (AKI) and fluid overload (FO). Contemporary reports on pediatric CRRT are small and single center in design. Large international studies evaluating CRRT practice and outcomes are lacking. Herein, we describe the design of a multinational collaborative. Methods: The Worldwide Exploration of Renal Replacement Outcomes Collaborative in Kidney Disease (WE-ROCK) is an international collaborative of pediatric specialists whose mission is to improve short- and long-term outcomes of children treated with CRRT. The aims of this multicenter retrospective study are to describe the epidemiology, liberation patterns, association of fluid balance and timing of CRRT initiation, and CRRT prescription with outcomes. Results: = 996, 0-25 years) admitted to an intensive care unit (ICU) and treated with CRRT for AKI or FO at 32 centers (in 7 countries) from 2018 to 2021. Demographics and clinical characteristics before CRRT initiation, during the first 7 days of both CRRT, and liberation were collected. Outcomes include the following: (i) major adverse kidney events at 90 days (mortality, dialysis dependence, and persistent kidney dysfunction), and (ii) functional outcomes (functional stats scale). Conclusion: The retrospective WE-ROCK study represents the largest international registry of children receiving CRRT for AKI or FO. It will serve as a broad and invaluable resource for the field of pediatric critical care nephrology that will improve our understanding of practice heterogeneity and the association of CRRT with clinical and patient-centered outcomes. This will generate preliminary data for future interventional trials in this area.

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)0.001
metaresearch head score (Gemma)0.014
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Insufficient 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: none
Teacher disagreement score0.646
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.014
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0010.001
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 head0.040
GPT teacher head0.368
Teacher spread0.328 · 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

Citations35
Published2023
Admission routes1
Has abstractyes

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