Nutritional status in children and adolescents with leukemia: An emphasis on clinical outcomes in low and middle income countries
Bibliographic record
Abstract
OBJECTIVE: The purpose of this narrative review is to examine the information available on the nutritional status of children with leukemia in low and middle income countries (LMICs), where the great majority of them live and malnutrition is prevalent, in order to identify best practices and remaining deficits in knowledge. METHODS: Literature relevant to measurement of nutritional status and the impact of nutritional status on important clinical outcomes in this population, and others of relevance, was reviewed. RESULTS: Arm anthropometry provides more accurate information on nutritional status than measures based on body weight in children with cancer. Both over- and under-nutrition are important determinants of tolerance of chemotherapy, compliance with treatment, relapse of disease, and survival. These relationships are subject to change with nutritional intervention. There are valuable roles for educational tools and 'ready-to-use-therapeutic-foods'. DISCUSSION: Assessment of nutritional status is mandatory in this population and accomplishable at various levels of sophistication according to available resources. Recognition of the fundamental role of nutritional status in affecting outcomes in children with leukemia is expanding, but knowledge gaps remain. An apparently counter-intuitive strategy of caloric restriction may be worthy of exploration. There is a particular need to establish normative data, including measures of body composition, in children in LMICs. CONCLUSIONS: Developing adaptive clinical practice guidelines for the measurement of nutritional status and for nutritional interventions, incorporating assessment of health-related quality of life, are evident priorities in the care of children with leukemia in LMICs.
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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 | 0.001 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".