Children's Oncology Group's 2023 blueprint for research: Germ cell tumors
Bibliographic record
Abstract
Extracranial germ cell tumors (GCT) are a biologically diverse group of tumors occurring in children, adolescents, and young adults. The majority of patients have excellent outcomes, but treatment-related toxicities impact their quality of survivorship. A subset of patients succumbs to the disease. Current unmet needs include clarifying which patients can be safely observed after initial surgical resection, refinement of risk stratification to reduce chemotherapy burden in patients with standard-risk disease, and intensify therapy for patients with poor-risk disease. Furthermore, enhancing strategies for detection of minimal residual disease and early detection of relapse, particularly in serum tumor marker-negative histologies, is critical. Improving the understanding of the developmental and molecular origins of GCTs may facilitate discovery of novel targets. Future efforts should be directed toward assessing novel therapies in a biology-driven, biomarker-defined, histology-specific, risk-stratified patient population. Fragmentation of care between subspecialists restricts the unified study of these rare tumors. It is imperative that trials be conducted in collaboration with national and international cooperative groups, with harmonized data and biospecimen collection. Key priorities for the Children's Oncology Group (COG) GCT Committee include (a) better understanding the biology of GCTs, with a focus on molecular targets and mechanisms of treatment resistance; (b) strategic development of pediatric and young adult clinical trials; (c) understanding late effects of therapy and identifying individuals most at risk; and (d) prioritizing diversity, equity, and inclusion to reduce cancer health disparities and studying the impacts of social determinants of health on outcomes.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.003 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.007 | 0.004 |
| Open science | 0.003 | 0.004 |
| Research integrity | 0.020 | 0.017 |
| Insufficient payload (model declined to judge) | 0.095 | 0.066 |
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".