Children's Oncology Group's 2023 blueprint for research: Central nervous system tumors
Bibliographic record
Abstract
Tumors of the central nervous system (CNS) are a leading cause of morbidity and mortality in the pediatric population. Molecular characterization in the last decade has redefined CNS tumor diagnoses and risk stratification; confirmed the unique biology of pediatric tumors as distinct entities from tumors that occur in adulthood; and led to the first novel targeted therapies receiving Food and Drug Administration (FDA) approval for children with CNS tumors. There remain significant challenges to overcome: children with unresectable low-grade glioma may require multiple prolonged courses of therapy affecting quality of life; children with high-grade glioma have a dismal long-term prognosis; children with medulloblastoma may suffer significant short- and long-term morbidity from multimodal cytotoxic therapy, and approaches to improve survival in ependymoma remain elusive. The Children's Oncology Group (COG) is uniquely positioned to conduct the next generation of practice-changing clinical trials through rapid prospective molecular characterization and therapy evaluation in well-defined clinical and molecular groups.
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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 | 0.005 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.013 | 0.017 |
| Insufficient payload (model declined to judge) | 0.038 | 0.026 |
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".