Reflecting on scientific growth and innovation as the Society for Neuro-Oncology turns 30
Bibliographic record
Abstract
The Society for Neuro-Oncology (SNO) marks its 30th anniversary in 2025, providing an opportunity to reflect on scientific advances and future directions in the field. Over 3 decades, SNO has catalyzed scientific innovation, education, mentorship, and global collaboration, advancing the care of patients with primary and metastatic brain tumors. Through its annual meeting and subspecialty conferences in pediatric neuro-oncology and brain metastases, as well as its journals, including Neuro-Oncology, Neuro-Oncology Practice, Neuro-Oncology Advances, and the recently launched Neuro-Oncology Pediatrics, SNO has established leading platforms for disseminating knowledge, sharing best practices, and shaping clinical, translational, and basic research worldwide. Scientific milestones during this period include the integration of molecular profiling into central nervous system tumor classification, advances in neuroimaging for diagnosis and treatment monitoring, targeted therapies for selected glioma patients, and the evolution of brain metastases management from whole-brain radiotherapy to multimodal strategies that incorporate targeted and immune-based therapies. Pediatric neuro-oncology has similarly advanced with the use of histomolecular diagnostics, refined risk stratification, and the development of novel targeted agents, alongside an increased emphasis on survivorship. Looking forward, emerging insights into the tumor microenvironment and novel immunotherapeutic approaches offer promising directions for future discovery and translation.
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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 | 0.020 | 0.037 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.003 | 0.007 |
| Scholarly communication | 0.023 | 0.015 |
| Open science | 0.002 | 0.013 |
| Research integrity | 0.014 | 0.017 |
| Insufficient payload (model declined to judge) | 0.037 | 0.032 |
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".