Bevacizumab in Pediatric Neuro-Oncology
Bibliographic record
Abstract
Bevacizumab is often used off-label in pediatric neuro-oncology, and evidence for indications of bevacizumab use in pediatric neuro-oncology is often fragmented. Therefore, this review aims to provide an organized summary of efficacy across different types of tumors, highlight outcomes, and link findings to the underlying biology. Gaps in the literature were also identified to guide future research. We narratively synthesized various pediatric studies, and the following tumor categories were identified for discussion: low-grade glioma, high-grade glioma, diffuse intrinsic pontine glioma, schwannoma, medulloblastoma, radiation necrosis, and cerebral edema. Key outcomes considered included overall survival, event-free survival, progression-free survival, vision and/or hearing improvements, steroid use, quality of life, and toxicity. The greatest benefits were observed in cases such as recurrent medulloblastoma in combination with temozolomide and irinotecan, optic pathway glioma visual function, and diminished steroid use in radiation necrosis. Results were poorer in cases of newly diagnosed high-grade gliomas and diffuse intrinsic pontine gliomas. The medication was overall well tolerated, with adverse events like hypertension, proteinuria, and epistaxis often being manageable with surveillance. In consideration of the results, bevacizumab should be considered based on the tumor profile, and its outcome measured along functional endpoints, besides radiological evolution. Continued investigations into outcome measures, as well as combination with targeted treatments and optimizing therapy, will contribute to improving outcomes in this vulnerable population.
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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) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.002 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.003 | 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".