Nutrition in Children with Kidney Disease: Pitfalls of Popular Assessment Methods
Bibliographic record
Abstract
Children with chronic kidney disease (CKD) are considered at high risk for protein-energy malnutrition. Clinical practice guidelines generally recommend an evaluation of numerous nutritional parameters to give a complete and accurate picture of nutritional status. This review summarizes the potential limitations of commonly used methods of nutritional assessmentin the setting of CKD. Unrecognized fluid overload and inappropriate normalization of body composition measures are the most important factors leading to misinterpretation of the nutritional assessment in CKD. The importance of expressing body composition measures relative to height or height-age in a population in whom short stature and pubertal delay are highly prevalent is emphasized. The limitations of growth as a marker for nutritional status are also addressed. In addition, the prevailing belief that children with CKD are at high risk for malnutrition is challenged.
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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 | 0.095 | 0.152 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.005 | 0.004 |
| Science and technology studies | 0.001 | 0.007 |
| Scholarly communication | 0.005 | 0.006 |
| Open science | 0.004 | 0.005 |
| Research integrity | 0.002 | 0.006 |
| Insufficient payload (model declined to judge) | 0.001 | 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".