INNV-35. IMPLEMENTATION OF A NATIONAL ADOLESCENT AND YOUNG ADULT MOLECULAR TUMOR BOARD: REPORT FROM THE CANADIAN AYA NEURO-ONCOLOGY NETWORK
Bibliographic record
Abstract
Abstract BACKGROUND The adolescent and young adult (AYA) population (15-39 years) is a unique and understudied population and their care requires multidisciplinary expertise. Current challenges in AYA neuro-oncology include fragmented care across pediatric and adult hospitals, low inclusion in clinical trials, unique survivorship considerations and a lack of standard-of-care therapies for this population. METHODS We will report on a two-and-a-half-year national effort from the Canadian AYA Neuro-Oncology Network (CANON) to implement national clinical and molecular AYA rounds. The aims of the rounds are to provide a platform for standardized diagnostics and multidisciplinary recommendations benefiting from the expertise of pediatric and adult providers across several tertiary and community centers. RESULTS Since April 2021, the Canadian national AYA rounds have occurred biweekly on a virtual secure platform. As of April 2023, 132 cases have been discussed from 25 sites; 15 cases (11%) involved pediatric patients and 117 (89%) were patients aged > 18 and treated at adult hospitals. Cases discussed included adult (n =34, 26%) and pediatric type (n = 22, 17%) low-(n=25, 19%) and high-grade (n=30, 23%) glioma, medulloblastoma (n=14, 10.5%), ependymoma (n=6, 5%) and other tumors (glioneuronal tumors, pineal tumors, germinoma). A total of 175 providers and 70 trainees across 32 Canadian institutions have attended the rounds, spanning adult and pediatric neurosurgery, neuropathology, neuro-oncology, radiation oncology and radiology. The updated results of an ongoing prospective database of cases presented at our AYA rounds, outlining themes around molecular characterization, choice of therapies and relevant clinical outcomes will be presented at the meeting. CONCLUSIONS Virtual national AYA-neuro-oncology multi-disciplinary rounds is an effective platform that has raised awareness of current gaps and has facilitated nation-wide solutions to improve the care of AYA brain tumors, leveraging research and clinical collaborations.
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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 | 0.005 | 0.011 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.005 | 0.001 |
| Scholarly communication | 0.002 | 0.000 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 0.000 |
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".