Nutritional management of the infant with chronic kidney disease stages 2–5 and on dialysis
Bibliographic record
Abstract
The nutritional management of children with chronic kidney disease (CKD) is of prime importance in meeting the challenge of maintaining normal growth and development in this population. The objective of this review is to integrate the Pediatric Renal Nutrition Taskforce clinical practice recommendations for children with CKD stages 2-5 and on dialysis, as they relate to the infant from full term birth up to 1 year of age, for healthcare professionals, including dietitians, physicians, and nurses. It addresses nutritional assessment, energy and protein requirements, delivery of the nutritional prescription, and necessary dietary modifications in the case of abnormal serum levels of calcium, phosphate, and potassium. We focus on the particular nutritional needs of infants with CKD for whom dietary recommendations for energy and protein, based on body weight, are higher compared with children over 1 year of age in order to support both linear and brain growth, which are normally maximal in the first 6 months of life. Attention to nutrition during infancy is important given that growth is predominantly nutrition dependent in the infantile phase and the growth of infants is acutely impaired by disruption to their nutritional intake, particularly during the first 6 months. Inadequate nutritional intake can result in the failure to achieve full adult height potential and an increased risk for abnormal neurodevelopment. We strongly suggest that physicians work closely with pediatric renal dietitians to ensure that the infant with CKD receives the best possible nutritional management to optimize their growth and development.
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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 | 0.000 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| 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) | 0.002 | 0.000 |
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.
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".