Pediatric low-grade glioma in the era of molecular diagnostics
Bibliographic record
Abstract
Low grade gliomas are the most frequent brain tumors in children and encompass a spectrum of histologic entities which are currently assigned World Health Organisation grades I and II. They differ substantially from their adult counterparts in both their underlying genetic alterations and in the infrequency with which they transform to higher grade tumors. Nonetheless, children with low grade glioma are a therapeutic challenge due to the heterogeneity in their clinical behavior - in particular, those with incomplete surgical resection often suffer repeat progressions with resultant morbidity and, in some cases, mortality. The identification of up-regulation of the RAS-mitogen-activated protein kinase (RAS/MAPK) pathway as a near universal feature of these tumors has led to the development of targeted therapeutics aimed at improving responses while mitigating patient morbidity. Here, we review how molecular information can help to further define the entities which fall under the umbrella of pediatric-type low-grade glioma. In doing so we discuss the specific molecular drivers of pediatric low grade glioma and how to effectively test for them, review the newest therapeutic agents and their utility in treating this disease, and propose a risk-based stratification system that considers both clinical and molecular parameters to aid clinicians in making treatment decisions.
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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 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".