Children, cancer, and nutrition—A dynamic triangle in review
Bibliographic record
Abstract
The overall cure rate for cancer in childhood now exceeds 70% and is projected to reach 85% by the year 2010 in industrialized countries. Therefore, major attention is being placed on reducing the side effects of therapy. However, 85% of the world's children live in developing countries, where access to adequate care often is limited and health status frequently is influenced adversely by prevalent infectious diseases and malnutrition. Despite several confounding factors (different definitions of nutritional status, the wide variety of measures used for its assessment, the selection biases by disease and stage, treatment protocols of variable dose intensity and efficacy, small sample sizes of the studies conducted in the last 20 years), it is accepted that the prevalence of malnutrition at diagnosis averages 50% in children with cancer in developing countries; whereas, in industrialized countries, it is related to the type of tumor and the extent of the disease, ranging from < 10% in patients with standard-risk acute lymphoblastic leukemia to 50% in patients with advanced neuroblastoma. The importance of nutritional status in children with cancer is related to its possible influence on the course of the disease and survival. Some authors have described decreased tolerance of chemotherapy associated with altered metabolism of antineoplastic drugs, increased infection rates, and poor clinical outcome in malnourished children. In this article, the authors review methods of nutritional assessment and the pathogenesis of nutritional morbidity in children with cancer as well as correlations of nutritional status with diagnosis, treatment, and outcome.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.004 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.002 | 0.003 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.003 |
| Insufficient payload (model declined to judge) | 0.003 | 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".