Virtual CNS Tumor Board Rounds Within the Canadian Adolescent and Young Adult Neuro-Oncology Network
Bibliographic record
Abstract
PURPOSE CNS tumors are a significant cause of death in the adolescent and young adult (AYA; age 15-39 years) population; however, these patients often lack standardized treatments. METHODS In Canada, we have established national multidisciplinary virtual AYA CNS tumor board rounds (national rounds) to improve and standardize care. From November 2021 to June 2024, 185 AYA patients with CNS tumors were presented from centers nationwide, including 138 patients with glioma. RESULTS Before case presentation, 5.1% of patients with glioma were taking targeted agents or were enrolled in clinical trials. However, after national rounds, 72.6% of patients with pediatric-type glioma and 45.9% of patients with adult-type glioma were recommended clinical trials and/or targeted agents. Among the 44 patients with glioma who had received radiation therapy before national rounds, only 14 were recommended further radiation. Cumulatively, 68.9% of patients analyzed received a treatment recommendation that represented a change in clinical management compared with their previous treatments. Concurrently, we performed molecular review of 174 AYA CNS tumors during the study time frame. Using TruSight, we identified gene fusions involving BRAF and FGFR1 in 17.9% and 7.4% of patients with low-grade glioma, respectively. Moreover, single-nucleotide variants in BRAF and FGFR1 were detected in 7.4% and 12.6% of patients with low-grade glioma, respectively. CONCLUSION Our results suggest that national rounds with centralized molecular review can direct AYA patients with CNS tumors toward targeted agents and clinical trials, while deferring radiation therapy. Taken together, our work details an ongoing effort to improve and standardize care of AYA patients with CNS tumors in Canada.
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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 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.008 | 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".