Optimizing preclinical pediatric low-grade glioma models for meaningful clinical translation
Bibliographic record
Abstract
Pediatric low-grade gliomas (pLGGs) are the most common brain tumor in young children. While they are typically associated with good overall survival, children with these central nervous system tumors often experience chronic tumor- and therapy-related morbidities. Moreover, individuals with unresectable tumors frequently have multiple recurrences and persistent neurological symptoms. Deep molecular analyses of pLGGs reveal that they are caused by genetic alterations that converge on a single mitogenic pathway (MEK/ERK), but their growth is heavily influenced by nonneoplastic cells (neurons, T cells, microglia) in their local microenvironment. The interplay between neoplastic cell MEK/ERK pathway activation and stromal cell support necessitates the use of predictive preclinical models to identify the most promising drug candidates for clinical evaluation. As part of a series of white papers focused on pLGGs, we discuss the current status of preclinical pLGG modeling, with the goal of improving clinical translation for children with these common brain tumors.
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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 | 0.002 | 0.002 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.002 |
| Insufficient payload (model declined to judge) | 0.004 | 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".