Southern European Prospective Investigation Into Childhood Cancer and Nutrition (EPICkids): Study design and protocol
Bibliographic record
Abstract
The survival rates for children with cancer have increased appreciably over the last few decades; however, childhood cancer survivors continue to suffer from long-lasting sequelae. Studies have demonstrated that the presence of malnutrition, over- and under-nutrition, at diagnosis or the duration of malnutrition during treatment is associated with increased toxicity, infection, and inferior survival. Dietary habits, along with behavioral and socioeconomic status, are known factors that lead to obesity or undernutrition and can affect the prognosis and quality of life of children with cancer. Unfortunately, the underlying mechanisms responsible for these observations are largely unknown. To address this gap in science, we established the EPICkids cohort study, an initiative of the International Initiative for Pediatrics and Nutrition at Columbia University Irving Medical Center and the International Agency for Research on Cancer of the World Health Organization. Over a 5-year period, children and adolescents with acute lymphoblastic leukemia and brain tumors receiving treatment in Spain, Italy, or Greece will be recruited. Clinical data and biospecimens (blood and stool) will be collected at designated timepoints in therapy. At the same time, several surveys will be administered to collect data on sociodemographics, physical activity, quality of life, food insecurity, and dietary habits. The primary aim of EPICkids is to develop a large informative nutrition biobank and database to investigate the etiologic pathways that connect nutritional status and lifestyle factors with clinical outcomes in children and adolescents with cancer. Secondary aims are to create evidence-based guidelines for European children with cancer in this understudied region and to ultimately improve the quality of life of those children and adolescents. The ClinicalTrials.gov ID for EPICkids study is NCT05375617.
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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 | 0.015 | 0.011 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.003 | 0.002 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.003 | 0.002 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.048 | 0.009 |
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".