Developing a Healthy Web-Based Cookbook for Pediatric Cancer Patients and Survivors: Rationale and Methods
Bibliographic record
Abstract
BACKGROUND: Obesity has been a growing problem among children and adolescents in the United States for a number of decades. Childhood cancer survivors (CCS) are more susceptible to the downstream health consequences of obesity such as cardiovascular disease, endocrine issues, and risk of cancer recurrence due to late effects of treatment and suboptimal dietary and physical activity habits. OBJECTIVE: The objective of this study was to document the development of a Web-based cookbook of healthy recipes and nutrition resources to help enable pediatric cancer patients and survivors to lead healthier lifestyles. METHODS: The Web-based cookbook, named "@TheTable", was created by a committee of researchers, a registered dietitian, patients and family members, a hospital chef, and community advisors and donors. Recipes were collected from several sources including recipe contests and social media. We incorporated advice from current patients, parents, and CCS. RESULTS: Over 400 recipes, searchable by several categories and with accompanying nutritional information, are currently available on the website. In addition to healthy recipes, social media functionality and cooking videos are integrated into the website. The website also features nutrition information resources including nutrition and cooking tip sheets available on several subjects. CONCLUSIONS: The "@TheTable" website is a unique resource for promoting healthy lifestyles spanning pediatric oncology prevention, treatment, and survivorship. Through evaluations of the website's current and future use, as well as incorporation into interventions designed to promote energy balance, we will continue to adapt and build this unique resource to serve cancer patients, survivors, and the general public.
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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 | 0.009 | 0.012 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.000 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.015 | 0.005 |
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".