Homemade formulas for nutrition support in chronic kidney disease: A narrative review of the opportunity for education, research, and innovation
Bibliographic record
Abstract
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 machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.005 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
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".