Central nervous system tumors in adolescents and young adults: A Society for Neuro-Oncology Consensus Review on diagnosis, management, and future directions
Bibliographic record
Abstract
Adolescents and young adults (AYAs; ages 15-39 years) are a vulnerable population facing challenges in oncological care, including access to specialized care, transition of care, unique tumor biology, and poor representation in clinical trials. Brain tumors are the second most common tumor type in AYA, with malignant brain tumors being the most common cause of cancer-related death. The 2021 WHO Classification for central nervous system (CNS) Tumors highlights the importance of integrated molecular characterization with histologic diagnosis in several tumors relevant to the AYA population. In this position paper from the Society for Neuro-Oncology (SNO), the diagnosis and management of CNS tumors in AYA is reviewed, focusing on the most common tumor types in this population, namely glioma, medulloblastoma, ependymoma, and CNS germ cell tumor. Current challenges and future directions specific to AYA are also highlighted. Finally, possible solutions to address barriers in the care of AYA patients are discussed, emphasizing the need for multidisciplinary and collaborative approaches that span the pediatric and adult paradigms of care, and incorporating advanced molecular testing, targeted therapy, and AYA-centered care.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.003 | 0.003 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 0.001 |
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".