Management of Inoperable Supra-Sellar Low-Grade Glioma With BRAF Mutation in Young Children
Bibliographic record
Abstract
Pediatric low-grade gliomas (PLGGs) are the most common central nervous system (CNS) tumors in children. The current standard of care for surgically unresectable and/or progressive cases of PLGGs includes combination chemotherapy. PLGGs are molecularly characterized by alterations in the RAS/RAF/MAPK/ERK pathway in a majority of tumors. PLGGs harboring the BRAF-V600E mutation respond poorly to current chemotherapy strategies. We present a case of a two-year-old female with biopsy-proven low-grade glioma (LGG, pilocytic astrocytoma) involving the hypothalamic/optic chiasm region. At presentation, she had obstructive hydrocephalus, bitemporal hemianopia, central hypothyroidism, and right-sided hemiparesis due to the location/mass effect of the tumor. She was initially treated with chemotherapy (vincristine/carboplatin), but her tumor progressed at six weeks of treatment. She was subsequently started on dabrafenib as her tumor was positive for BRAF-V600E mutation. Dabrafenib monotherapy resulted in dramatic improvement in her clinical symptoms and near-complete resolution of tumor. Our experience and review of the literature suggest that LGGs with BRAF-V600E mutations may benefit from upfront targeted therapy in children. There is an urgent need for prospective clinical trials comparing the efficacy of upfront BRAF inhibitors versus standard chemotherapy in PLGGs with BRAF mutations.
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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 | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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".