Integrated clinical and molecular landscape of disseminated pediatric low-grade glioma
Bibliographic record
Abstract
ABSTRACT: BackgroundPediatric-type low-grade gliomas (PLGG) are the most common central nervous system (CNS) tumor in children. Many are indolent and have excellent outcomes; however, some inexplicably spread throughout the CNS leading to increased morbidity and mortality. METHODS: To better understand this rare and difficult-to-treat entity, as well as the features associated with dissemination in CNS tumors, we assembled a large international cohort (n = 269) of patients with disseminated PLGG with detailed clinical and molecular characterization, including DNA sequencing and methylome profiling. RESULTS: We identified three subgroups of patients based on the temporal and spatial distribution of dissemination. Tumors with diffuse leptomeningeal spread without a primary tumor mass and those occurring in infants had the worst clinical outcomes. The genetics overlapped substantially with that of non-disseminated PLGG, suggesting that non-genetic mechanisms are an important contributor to dissemination. Therapeutically, targeted RAS/MAPK-pathway inhibition was more effective than conventional chemotherapy as first or second-line treatment. CONCLUSIONS: In sum, this cohort increases our clinical and biological understanding of this rare disease, provides insights for improving patient care, and directs future clinical trials and basic science research.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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".