Management of high-grade gliomas in the pediatric patient: Past, present, and future
Bibliographic record
Abstract
High-grade gliomas (HGGs) constitute ∼15% of all primary brain tumors in children and adolescents. Routine histopathological diagnosis is based on tissue obtained from biopsy or, preferably, from the resected tumor itself. The majority of pediatric HGGs are clinically and biologically distinct from histologically similar adult malignant gliomas; these differences may explain the disparate responses to therapy and clinical outcomes when comparing children and adults with HGG. The recently proposed integrated genomic classification identifies 6 distinct biological subgroups of glioblastoma (GBM) throughout the age spectrum. Driver mutations in genes affecting histone H3.3 (K27M and G34R/V) coupled with mutations involving specific proteins (TP53, ATRX, DAXX, SETD2, ACVR1, FGFR1, NTRK) induce defects in chromatin remodeling and may play a central role in the genesis of many pediatric HGGs. Current clinical practice in pediatric HGGs includes surgical resection followed by radiation therapy (in children aged > 3 years) with concurrent and adjuvant chemotherapy with temozolomide. However, these multimodality treatment strategies have had a minimal impact on improving survival. Ongoing clinical trials are investigating new molecular targets, chemoradiation sensitization strategies, and immunotherapy. Future clinical trials of pediatric HGG will incorporate the distinction between GBM molecular subgroups and stratify patients using group-specific biomarkers.
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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 | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| 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".