Nutritional Assessment of Children With Cancer
Bibliographic record
Abstract
Regardless of which parts of the world they live in, most children will develop and grow at a similar rate if proper nutrition is ensured. Children from developing countries are at risk for primary malnutrition. Children undergoing anticancer therapy are at higher risk for secondary malnutrition, including obesity and growth retardation. Periodic nutritional assessments are important for planning effective dietary interventions for such children. In this review, we describe malnutrition as it occurs in children with cancer and various ways of assessing the nutritional status of these children, depending on the availability of resources in their local hospitals. Objective and subjective data should be used to complete the nutritional assessment. We discuss screening methods, including the use of subjective global assessment. Different parts of nutritional assessment include medical history; physical examination; biochemical and hematological data, such as visceral proteins, blood glucose levels, and lipid profiles, hemoglobin and hematocrit, and the lymphocyte count; anthropometric measurements; and food and nutrition history. We review medical tests and procedures to determine nutritional status, including nitrogen balance, delayed cutaneous hypersensitivity, prognostic nutritional index, creatinine height index, maldigestion and malabsorption tests, indirect calorimetry, and dual energy X ray absorptiometry (DXA scan). Evaluation and interpretation of data and estimation of nutritional risk are discussed, including proper techniques and use of anthropometric measures, selection and use of growth charts, calculation of caloric and protein needs, and the percentage of calories ingested. These methods will enable local health care providers to accurately assess the nutritional status of children with cancer, identify children at risk, and plan adequate nutritional interventions.
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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 | 0.000 |
| 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".