OTHR-11. Virtual CNS tumor board rounds within the Canadian Adolescent and Young Adult Neuro-oncology Network (CANON)
Bibliographic record
Abstract
Abstract Central nervous system (CNS) tumors are the leading cause of cancer-related death in the adolescent and young adult (AYA; ages 15 – 39) population. CNS tumors in AYAs are biologically and clinically distinct as they intersect those seen in younger children and older adults, and there is a lack of standardized treatment approaches for such patients. To improve clinical management of this patient population in Canada, virtual AYA CNS tumor boards were established in which a multidisciplinary team of pediatric and adult physicians review challenging cases and make evidence-based suggestions regarding optimal care. From November 2021 to June 2024, 185 AYA CNS tumor patients were presented at biweekly tumor board rounds. Patient medical history and tumor board recommendation information was de-identified, stored in a REDCap database and subsequently analyzed. Concurrent weekly molecular rounds at the Hospital for Sick Children (SickKids) performed genetic and epigenetic analysis of AYA CNS tumors. Most patients presented (n = 138) had a glioma diagnosis, with pediatric diffuse low-grade glioma being the commonest subtype (n = 59). Recommendations made by the AYA CNS tumor board often represented a change in clinical management from the patient’s previous treatment course. A large subset of glioma patients were directed toward clinical trials and targeted agents and were spared further exposure to radiation therapy. Concurrent molecular profiling of 157 AYA gliomas identified targetable genetic alterations including BRAF fusions and FGFR1 fusions and mutations. Feedback from meeting attendees indicated that attending AYA CNS tumor board rounds was a valuable experience. Our work represents an effort to standardize and improve care for AYA CNS tumor patients in Canada through recurring, biweekly interdisciplinary tumor boards. Our results suggest that AYA CNS tumor board recommendations are effective in guiding patients towards clinical trials and targeted agents, and are a valued resource by Canadian clinicians.
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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 | 0.002 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.003 | 0.001 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.068 | 0.011 |
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".