The relevance of nutrition to pediatric oncology: A cancer control perspective
Bibliographic record
Abstract
It is indisputable that adequate and appropriate nutrition is fundamental to the health, growth, and development of infants, children, and adolescents, including those with cancer. Nutrition has a role in most of the accepted components of the cancer control spectrum, from prevention through to palliation. The science of nutrigenomics, nutrigenetics, and bioactive foods (phytochemicals), and how nutrition affects cancer biology and cancer treatment, is growing. Nutritional epigenetics is giving us an understanding that there are possible primary prevention strategies for pediatric cancers, especially during conception and pregnancy, which need to be studied. Primary prevention of cancer in adults, such as colorectal cancer, should commence early in childhood, given the long gestation of nutritionally related cancers. Obesity avoidance is definitely a target for both pediatric and adult cancer prevention, commencing in childhood. There is now compelling evidence that the nutritional status of children with cancer, both overweight and underweight, does affect cancer outcomes. This is a potentially modifiable prognostic factor. Consistent longitudinal nutritional assessment of patients from diagnosis through treatment and long-term follow-up is required so that interventions can be implemented and evaluated. While improving, there remains a dearth of basic and clinical nutritional research in pediatric oncology. The perspective of evaluating nutrition as a cancer control factor is discussed in this article.
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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 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 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".