The influence of nutrition on clinical outcomes in children with cancer
Bibliographic record
Abstract
Adequate and appropriate nutrition is essential for growth and development in children; all put at risk in those with cancer. Overnutrition and undernutrition at diagnosis raise the risk of increased morbidity and mortality during therapy and beyond. All treatment modalities can jeopardize nutritional status with potentially adverse effects on clinical outcomes. Accurate assessment of nutritional status and nutrient balance is essential, with remedial interventions delivered promptly when required. Children with cancer in low- and middle-income countries (LMICs) are especially disadvantaged with concomitant challenges in the provision of nutritional support. Cost-effective advances in the form of ready-to-use therapeutic foods (RUTF) may offer solutions. Studies in LMICs have defined a critical role for the gut microbiome in the causation of undernutrition in children and have demonstrated a beneficial effect of selected RUTF in redressing the imbalanced microbiota and improving nutritional status. Challenges in high-income countries relate both to concerns about the potential disadvantage of preexisting obesity in those newly diagnosed and to undernutrition identified at diagnosis and during treatment. Much remains to be understood but the prospects are bright for offsetting malnutrition in children with cancer, resulting in enhanced opportunity for healthy survival.
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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) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".