Assessment and management of magnesium and trace element status in children with CKD stages 2–5, on dialysis and post-transplantation: Clinical practice points from the Pediatric Renal Nutrition Taskforce
Bibliographic record
Abstract
Children and young people with chronic kidney disease (CKD) are at risk for deficiency or excess of magnesium and trace elements. Kidney function, dialysis, medication, and dietary and supplemental intake can affect their biochemical status. There is much uncertainty about the requirements of magnesium and trace elements in CKD, which leads to variation in practice. The Pediatric Renal Nutrition Taskforce is an international team of pediatric kidney dietitians and pediatric nephrologists, formed to develop evidence-based clinical practice points to improve the nutritional care of children with CKD. PICO (patient, intervention, comparator, and outcomes) questions led the literature searches, which were conducted to ascertain current biochemical status, dietary intake, and factors leading to requirements differing from healthy peers, and to guide nutritional care of children with CKD stages 2-5, on dialysis, and post-transplantation. We address the assessment and intervention of magnesium and the trace elements chromium, copper, fluoride, iodine, manganese, selenium, and zinc. We suggest routine biochemical assessment of magnesium. Trace element assessment is based on clinical suspicion of deficiency or excess and their risk factors, including accumulation, losses, medications, nutrient interactions, and comorbidities. In particular, we suggest assessing magnesium, copper, iodine, and zinc when growth is poor, and evaluating magnesium, copper, selenium, and zinc in the presence of proteinuria. A structured approach to magnesium and trace element management, including biochemical, physical, and dietary assessment, is beneficial in the paucity of evidence. Research recommendations are suggested.
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 imitationNot 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.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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 teacher head, 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".