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Record W4407058763 · doi:10.1002/ncp.11271

Homemade formulas for nutrition support in chronic kidney disease: A narrative review of the opportunity for education, research, and innovation

2025· review· en· W4407058763 on OpenAlexaff
Brandon M. Kistler, Annabel Biruete, Michelle Wong, Angela Yee‐Moon Wang, Fabiola Martín-del-Campo, Fabiana Baggio Nerbass, Anna K. Hardy, Qiwei Zhu, Ban‐Hock Khor, Lloyd Vincent, Zarina Ebrahim, Ana Elizabeth Figueiredo

Bibliographic record

VenueNutrition in Clinical Practice · 2025
Typereview
Languageen
FieldMedicine
TopicDialysis and Renal Disease Management
Canadian institutionsUniversity of British Columbia
FundersNational Center for Advancing Translational SciencesNational Institutes of Health
KeywordsMedicineNarrative reviewNarrativeIntensive care medicineKidney diseaseGerontologyInternal medicineLinguistics

Abstract

fetched live from OpenAlex

Protein-energy wasting is common in people with chronic kidney disease (CKD), especially in those undergoing kidney replacement therapy. Oral nutrition supplements and enteral nutrition are strategies that have been shown to improve nutrition status, and potentially outcomes. However, access to specialized commercial products for people with CKD is limited by factors including cost and regional availability. Homemade formulas represent a potentially cheaper, accessible, and more flexible option than commercial products, but they come with their own unique set of challenges. Furthermore, some aspects of homemade products, including consistency of nutrients, physical properties, and food safety, may pose challenges in the context of physiological changes that occur in CKD. Despite evidence of their use in CKD clinics, there have been few studies using homemade formulas in this population. This narrative review article summarizes the available literature on the potential usage, benefits, and concerns related to homemade formulas, emphasizing the unique challenges in people with CKD. Given the potential usage and limited research on homemade formulas in people with CKD, additional education and research are warranted to optimize the use of these tools in this clinical population.

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)0.008
metaresearch head score (Gemma)0.074
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesMetaresearch
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Systematic review · Consensus signal: none
GenreCandidate signal: Review · Consensus signal: Review
Teacher disagreement score0.537
Threshold uncertainty score0.997

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0080.074
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0010.003
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 head0.244
GPT teacher head0.573
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 designSystematic review
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

Citations0
Published2025
Admission routes1
Has abstractyes

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