Canadian Consensus for Treatment of BRAF V600E Mutated Pediatric and AYA Gliomas
Bibliographic record
Abstract
Background: The treatment of BRAF V600E gliomas with BRAF inhibitors (BRAFis) and MEK inhibitors (MEKis) has been increasingly integrated into clinical practice for pediatric low-grade gliomas (PLGGs) and pediatric high-grade gliomas (HGGs). However, some questions remain unanswered, such as the best time to start targeted therapy, duration of treatment, and discontinuation of therapy. Given that no clinical trial has been able to address these critical questions, we developed a Canadian Consensus statement for the treatment of BRAF V600E mutated pediatric as well as adolescent and young adult (AYA) gliomas. Methods: Canadian neuro-oncologists were invited to participate in the development of this consensus. The consensus was discussed during monthly web-based national meetings, and the algorithms were revised until a consensus was achieved. Results: A total of 26 participants were involved in the development of the algorithms. Two treatment algorithms are proposed, one for the initiation of treatment and one for the discontinuation of treatment. We suggest that most patients with BRAF V600E gliomas should be treated with BRAFis ± MEKis upfront. Discontinuation of treatment can be considered in certain circumstances, and we suggest a slow wean. Conclusions: Based on expert consensus in Canada, we developed algorithms for treatment initiation of children and AYA with BRAF V600E gliomas as well as a discontinuation algorithm.
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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 | 0.022 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.005 | 0.002 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.005 | 0.003 |
| Research integrity | 0.003 | 0.005 |
| Insufficient payload (model declined to judge) | 0.006 | 0.002 |
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".