Children's Oncology Group's 2023 blueprint for research: Neuroblastoma
Bibliographic record
Abstract
Neuroblastoma is the most common extra-cranial solid tumor in children and is known for its clinical heterogeneity. A greater understanding of the biology of this disease has led to both improved risk stratification and new approaches to therapy. Outcomes for children with low and intermediate risk disease are excellent overall, and efforts to decrease therapy for such patients have been largely successful. Although survival has improved over time for patients with high-risk disease and treatments evaluated in the relapse setting are now being moved into earlier phases of treatment, much work remains to improve survival and decrease therapy-related toxicities. Studies of highly annotated biobanked samples continue to lead to important insights regarding neuroblastoma biology. Such studies, along with correlative biology studies incorporated into therapeutic trials, are expected to continue to provide insights that lead to new and more effective therapies. A focus on translational science is accompanied by an emphasis on new agent development, optimized risk stratification, and international collaboration to address questions relevant to molecularly defined subsets of patients. In addition, the COG Neuroblastoma Committee is committed to addressing the patient/family experience, mitigating late effects of therapy, and studying social determinants of health in patients with neuroblastoma.
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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 | 0.008 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.003 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.017 | 0.019 |
| Insufficient payload (model declined to judge) | 0.050 | 0.034 |
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".