Editorial: Targeted Therapies for Glioblastoma: A Critical Appraisal
Bibliographic record
Abstract
Targeted Therapies for Glioblastoma: A Critical AppraisalHigh grade gliomas including glioblastoma (GBM) in adults and diffuse intrinsic pontine glioma (DIPG) in children are fatal brain tumors with <5% of patients surviving 5 years after initial diagnosis and treatment.Targeted agents hold promise as monotherapy or as sensitizing strategies to improve response to traditional chemo-radiation.However, despite intense research endeavors and numerous clinical trials, no targeted agents have been FDA approved in the past decade.Clonal heterogeneity, acquired or inherent resistance to available therapies, restricted drug delivery, resistant stem-like cells, and immune-evasive properties in these tumors have been subject to intense study.Advances in technology including next-generation sequencing-has allowed comprehensive mapping of genetic alterations such as single nucleotide polymorphism, fusions, and copy number variations, and alterations to the epigenetic landscape including DNA methylation and histone post-translational modifications (PTMs).This approach has led to identification of several therapeutic targets and potential biomarkers, resulting in a number of new investigational treatment modalities.These include inhibitors of signaling, cell cycle and DNA damage repair pathways, and angiogenesis, in addition to ongoing evaluations of novel gene-, viro-, and immuno-therapies.However, as highlighted in the series of articles (referenced) and compiled in this eBook, despite significant progress in understanding pathology and molecular underpinnings, clinical development of novel therapeutics has faced challenges that hinder overall progress.
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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 | 0.009 | 0.033 |
| Meta-epidemiology (narrow) | 0.004 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.003 |
| Bibliometrics | 0.004 | 0.002 |
| Science and technology studies | 0.003 | 0.002 |
| Scholarly communication | 0.007 | 0.005 |
| Open science | 0.004 | 0.002 |
| Research integrity | 0.014 | 0.016 |
| Insufficient payload (model declined to judge) | 0.020 | 0.019 |
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".