The Power of Human Cancer Genetics as Revealed by Low-Grade Gliomas
Bibliographic record
Abstract
The human brain contains a vast number of cells and shows extraordinary cellular diversity to facilitate the many cognitive and automatic commands governing our bodily functions. This complexity arises partly from large-scale structural variations in the genome, evolutionary processes to increase brain size, function, and cognition. Not surprisingly given recent technical advances, low-grade gliomas (LGGs), which arise from the glia (the most abundant cell type in the brain), have undergone a recent revolution in their classification and therapy, especially in the pediatric setting. Next-generation sequencing has uncovered previously unappreciated diverse LGG entities, unraveling genetic subgroups and multiple molecular alterations and altered pathways, including many amenable to therapeutic targeting. In this article we review these novel entities, in which oncogenic processes show striking age-related neuroanatomical specificity (highlighting their close interplay with development); the opportunities they provide for targeted therapies, some of which are already practiced at the bedside; and the challenges of implementing molecular pathology in the clinic.
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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 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 0.002 |
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".