Children’s Oncology Group’s 2023 Blueprint for Research: Behavioral Science
Bibliographic record
Abstract
As survival rates for childhood cancer have improved, there has been increasing focus on identifying and addressing adverse impacts of cancer and its treatment on children and their families during treatment and into survivorship. The Behavioral Science Committee (BSC) of the Children’s Oncology Group (COG), comprised of psychologists, neuropsychologists, social workers, nurses, physicians, and clinical research associates, aims to improve the lives of children with cancer and their families through research and dissemination of empirically supported knowledge. Key achievements of the BSC include enhanced interprofessional collaboration through integration of liaisons into other key committees within COG, successful measurement of critical neurocognitive outcomes through standardized neurocognitive assessment strategies, contributions to evidence-based guidelines, and optimization of patient-reported outcome measurement. The collection of neurocognitive and behavioral data continues to be an essential function of the BSC, in the context of therapeutic trials that are modifying treatments to maximize event-free survival, minimize adverse outcomes, and optimize quality of life. In addition, through hypothesis-driven research and multi-disciplinary collaborations, the BSC will also begin to prioritize initiatives to expand the systematic collection of predictive factors (e.g., social determinants of health) and psychosocial outcomes, with overarching goals of addressing health inequities in cancer care and outcomes, and promoting evidence-based interventions to improve outcomes for all children, adolescents, and young adults with cancer.
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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 | 0.018 | 0.037 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.002 | 0.004 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.002 | 0.005 |
| Research integrity | 0.009 | 0.010 |
| Insufficient payload (model declined to judge) | 0.093 | 0.058 |
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".