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Record W4388423072 · doi:10.1007/s00467-023-06186-4

Programs and processes for advancing pediatric acute kidney support therapy in hospitalized and critically ill children: a report from the 26th Acute Disease Quality Initiative (ADQI) consensus conference

2023· article· en· W4388423072 on OpenAlexaff
Tara M. Neumayr, Benan Bayrakçı, Rahul Chanchlani, Akash Deep, Jolyn Morgan, Ayse Akcan‐Arikan, Rajit K. Basu, Stuart L. Goldstein, David J. Askenazi, Rashid Alobaidi, Sean M. Bagshaw, Matthew Barhight, Erin F. Barreto, Oliver Ray, Erica C. Bjornstad, Patrick D. Brophy, Jennifer R. Charlton, Andrea L. Conroy, Prasad Devarajan, Kristin Dolan, Dana Y. Fuhrman, Katja M. Gist, Stephen M. Gorga, Jason H. Greenberg, Denise Hasson, Emma Heydari, Arpana Iyengar, Jennifer G. Jetton, Catherine D. Krawczeski, Leslie Meigs, Shina Menon, Catherine Morgan, Theresa Mottes, Zaccaria Ricci, David T. Selewski, Danielle E. Soranno, Natalja L. Stanski, Michelle C. Starr, Scott M. Sutherland, Jordan M. Symons, Marcelo Tavares, Molly Wong Vega, Michael Zappitelli, Claudio Ronco, Ravindra L. Mehta, John A. Kellum, Marlies Ostermann

Bibliographic record

VenuePediatric Nephrology · 2023
Typearticle
Languageen
FieldMedicine
TopicAcute Kidney Injury Research
Canadian institutionsMcMaster UniversityMcMaster Children's Hospital
FundersNational Institute of General Medical Sciences
KeywordsMedicineIntensive care medicineAcute kidney injuryCritically illDiseaseKidney diseaseNephrologyExtracorporealQuality managementMedical emergencyInternal medicine

Abstract

fetched live from OpenAlex

Pediatric acute kidney support therapy (paKST) programs aim to reliably provide safe, effective, and timely extracorporeal supportive care for acutely and critically ill pediatric patients with acute kidney injury (AKI), fluid and electrolyte derangements, and/or toxin accumulation with a goal of improving both hospital-based and lifelong outcomes. Little is known about optimal ways to configure paKST teams and programs, pediatric-specific aspects of delivering high-quality paKST, strategies for transitioning from acute continuous modes of paKST to facilitate rehabilitation, or providing effective short- and long-term follow-up. As part of the 26th Acute Disease Quality Initiative Conference, the first to focus on a pediatric population, we summarize here the current state of knowledge in paKST programs and technology, identify key knowledge gaps in the field, and propose a framework for current best practices and future research in paKST.

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)0.010
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch, Meta-epidemiology (narrow)
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.028
Threshold uncertainty score1.000

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.010
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.000
Bibliometrics0.0000.001
Science and technology studies0.0000.001
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 head0.041
GPT teacher head0.366
Teacher spread0.325 · 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 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

Citations9
Published2023
Admission routes1
Has abstractyes

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