DIPG in Children – What Can We Learn from the Past?
Bibliographic record
Abstract
Brainstem tumors represent 10-15% of pediatric central nervous system tumors and diffuse intrinsic pontine glioma (DIPG) is the most common brainstem tumor of childhood. DIPG is almost uniformly fatal and is the leading cause of brain tumor-related death in children. To date, radiation therapy (RT) is the only form of treatment that offers a transient benefit in DIPG. Chemotherapeutic strategies including multi-agent neoadjuvant chemotherapy, concurrent chemotherapy with RT, and adjuvant chemotherapy have not provided any survival advantage. To overcome the restrictive ability of the intact blood-brain barrier (BBB) in DIPG, several alternative drug delivery strategies have been proposed but have met with minimal success. Targeted therapies either alone or in combination with RT have also not improved survival. Five decades of unsuccessful therapies coupled with recent advances in the genetics and biology of DIPG have taught us several important lessons (1). DIPG is a heterogeneous group of tumors that are biologically distinct from other pediatric and adult high grade gliomas (HGG). Adapting chemotherapy and targeted therapies that are used in pediatric or adult HGG for the treatment of DIPG should be abandoned (2). Biopsy of DIPG is relatively safe and informative and should be considered in the context of multicenter clinical trials (3). DIPG probably represents a whole brain disease so regular neuraxis imaging is important at diagnosis and during therapy (4). BBB permeability is of major concern in DIPG and overcoming this barrier may ensure that drugs reach the tumor (5). Recent development of DIPG tumor models should help us accurately identify and validate therapeutic targets and small molecule inhibitors in the treatment of this deadly tumor.
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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 | 0.009 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.001 |
| Bibliometrics | 0.002 | 0.002 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.004 | 0.009 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.006 | 0.010 |
| Insufficient payload (model declined to judge) | 0.006 | 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".