Collaboration to Promote Research and Improve Clinical Care in the Evolving Field of Childhood Cancer Predisposition
Bibliographic record
Abstract
Germline pathogenic variants in cancer susceptibility genes are identified in up to 18% of all children with cancer. Because pediatric cancer predisposition syndromes (CPS) themselves are rare and underrecognized, there are limited data to guide the diagnosis and management of affected children and at-risk relatives. Furthermore, the care of affected children requires distinct considerations given the early onset of cancers, lifelong risks of additional cancers, and potential late effects of therapy. Herein, we discuss efforts to leverage existing infrastructure, organize experts, and develop a new consortium to optimize care and advance research for children with CPS. A 2016 workshop organized by the American Association for Cancer Research united many experts in childhood cancer predisposition and resulted in publication of multiple consensus guidelines for tumor surveillance. More recently, several of these authors established the Consortium for Childhood Cancer Predisposition (C3P), a multi-institutional collaboration that provides a structure for systematic research in cancer predisposition, screening, and prevention in children. The Consortium intends to work with other cooperative groups to merge longitudinal data from children with CPS throughout the continuum of the cancer risk period, as well as cancer treatment and survivorship care, to optimize overall outcomes.
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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 | 0.109 | 0.167 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.007 | 0.005 |
| Scholarly communication | 0.012 | 0.011 |
| Open science | 0.005 | 0.036 |
| Research integrity | 0.010 | 0.021 |
| Insufficient payload (model declined to judge) | 0.031 | 0.010 |
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".