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Record W4416308228 · doi:10.1007/s00467-025-06913-z

The dietary management of sodium in children with kidney diseases—clinical practice recommendations from the Pediatric Renal Nutrition Taskforce

2025· article· en· W4416308228 on OpenAlexaff
José Renken‐Terhaerdt, An Desloovere, Michiel J.S. Oosterveld, Nonnie Polderman, Evelien Snauwaert, Stella Stabouli, Johan Vande Walle, Caroline Anderson, Sheridan Collins, Larry A. Greenbaum, Matthew Harmer, Lyndsay A. Harshman, Christina L. Nelms, Pearl Pugh, Vanessa Shaw, Jetta Tuokkola, Molly Wong Vega, Bradley A. Warady, Rukshana Shroff, Fabio Paglialonga

Bibliographic record

VenuePediatric Nephrology · 2025
Typearticle
Languageen
FieldNursing
TopicSodium Intake and Health
Canadian institutionsBC Children's Hospital
FundersVitaflo
KeywordsSodiumDietary SodiumNephrologyDietary managementKidney diseaseKidneyUrine sodiumClinical Practice

Abstract

fetched live from OpenAlex

Sodium imbalance is a common concern in children with kidney diseases, presenting as either sodium excess or sodium deficit, each with significant clinical implications. Sodium excess contributes to fluid overload and hypertension, while increased sodium losses, particularly via urine or peritoneal fluid, can predispose patients to hypotension and growth failure. Effective sodium management is thus a critical component of care in pediatric kidney diseases, with dietary sodium intake playing a pivotal role in maintaining homeostasis. The Pediatric Renal Nutrition Taskforce (PRNT) has developed clinical practice recommendations (CPRs) for dietary sodium management in children with kidney diseases, addressing key aspects of sodium balance, including primary dietary sources, nutritional assessment of sodium intake, non-dietary factors influencing sodium balance, and recommended intakes. Strategies for reducing sodium intake in cases of sodium excess and augmenting intake in patients with increased sodium losses are also provided. The consensus of international experts was assessed through a Delphi process. These CPRs will be regularly updated by the PRNT.

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)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.142
Threshold uncertainty score0.459

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0010.000
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 integrity0.0000.001
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 head0.015
GPT teacher head0.327
Teacher spread0.312 · 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.

The models applied no category: nothing in the taxonomy fit this work.
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

Citations0
Published2025
Admission routes1
Has abstractyes

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