Advances in the molecular classification of pediatric brain tumors: a guide to the galaxy
Bibliographic record
Abstract
Central nervous system (CNS) tumors are the most common solid tumor in pediatrics, accounting for approximately 25% of all childhood cancers, and the second most common pediatric malignancy after leukemia. CNS tumors can be associated with significant morbidity, even those classified as low grade. Mortality from CNS tumors is disproportionately high compared to other childhood malignancies, although surgery, radiation, and chemotherapy have improved outcomes in these patients over the last few decades. Current therapeutic strategies lead to a high risk of side effects, especially in young children. Pediatric brain tumor survivors have unique sequelae compared to age-matched patients who survived other malignancies. They are at greater risk of significant impairment in cognitive, neurological, endocrine, social, and emotional domains, depending on the location and type of the CNS tumor. Next-generation genomics have shed light on the broad molecular heterogeneity of pediatric brain tumors and have identified important genes and signaling pathways that serve to drive tumor proliferation. This insight has impacted the research field by providing potential therapeutic targets for these diseases. In this review, we highlight recent progress in understanding the molecular basis of common pediatric brain tumors, specifically low-grade glioma, high-grade glioma, ependymoma, embryonal tumors, and atypical teratoid/rhabdoid tumor (ATRT). © 2020 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.
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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 | 0.003 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.006 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.001 | 0.004 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.002 | 0.005 |
| Insufficient payload (model declined to judge) | 0.005 | 0.007 |
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".