Children's Oncology Group 2023 blueprint: Nursing discipline
Bibliographic record
Abstract
In contrast to other Children's Oncology Group (COG) committees, the COG nursing discipline is unique in that it provides the infrastructure necessary for nurses to support COG clinical trials and implements a research agenda aimed at scientific discovery. This hybrid focus of the discipline reflects the varied roles and expertise within pediatric oncology clinical trials nursing that encompass clinical care, leadership, and research. Nurses are broadly represented across COG disease, domain, and administrative committees, and are assigned to all clinically focused protocols. Equally important is the provision of clinical trials-specific education and training for nurses caring for patients on COG trials. Nurses involved in the discipline's evidence-based practice initiative have published a wide array of systematic reviews on topics of clinical importance to the discipline. Nurses also develop and lead research studies within COG, including stand-alone studies and aims embedded in disease/ treatment trials. Additionally, the nursing discipline is charged with responsibility for developing patient/family educational resources within COG. Looking to the future, the nursing discipline will continue to support COG clinical trials through a multifaceted approach, with a particular focus on patient-reported outcomes and health equity/disparities, and development of interventions to better understand and address illness-related distress in children 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 | 0.027 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.007 | 0.008 |
| Scholarly communication | 0.010 | 0.006 |
| Open science | 0.003 | 0.011 |
| Research integrity | 0.021 | 0.029 |
| Insufficient payload (model declined to judge) | 0.025 | 0.019 |
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".