Children's Oncology Group's 2013 blueprint for research: Behavioral science
Bibliographic record
Abstract
Behavioral science has long played a central role in pediatric oncology clinical service and research. Early work focused on symptom relief related to side effects of chemotherapy and pain management related to invasive medical procedures. As survival rates improved, the focused has shifted to examination of the psychosocial impact, during and after treatment, of pediatric cancer and its treatment on children and their families. The success of the clinical trials networks related to survivorship highlights an even more critical role in numerous domains of psychosocial research and care. Within the cooperative group setting, the field of behavioral science includes psychologists, social workers, physicians, nurses, and parent advisors. The research agenda of this group of experts needs to focus on utilization of psychometrically robust measures to evaluate the impact of treatment on children with cancer and their families during and after treatment ends. Over the next 5 years, the field of behavioral science will need to develop and implement initiatives to expand use of standardized neurocognitive and behavior batteries; increase assessment of neurocognition using technology; early identification of at-risk children/families; establish standards for evidence-based psychosocial care; and leverage linkages with the broader behavioral health pediatric oncology community to translate empirically supported research clinical trials care to practice.
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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 | 0.022 | 0.036 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.011 | 0.012 |
| Science and technology studies | 0.002 | 0.005 |
| Scholarly communication | 0.006 | 0.006 |
| Open science | 0.003 | 0.005 |
| Research integrity | 0.010 | 0.019 |
| Insufficient payload (model declined to judge) | 0.010 | 0.006 |
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".