Bevacizumab for pediatric radiation necrosis
Bibliographic record
Abstract
BACKGROUND: Radiation necrosis is a frequent complication occurring after the treatment of pediatric brain tumors; however, treatment options remain a challenge. Bevacizumab is an anti-VEGF monoclonal antibody that has been shown in small adult cohorts to confer a benefit, specifically a reduction in steroid usage, but its use in children has not been well described. METHODS: We describe our experience with bevacizumab use for symptomatic radiation necrosis at 5 institutions including patients treated after both initial irradiation and reirradiation. RESULTS: We identified 26 patients treated with bevacizumab for symptomatic radiation necrosis, with a wide range of underlying diagnoses. The average age at diagnosis of radiation necrosis was 10.7 years, with a median time between the last dose of radiation and the presentation of radiation necrosis of 3.8 months (range, 0.6-110 months). Overall, we observed that 13 of 26 patients (50%) had an objective clinical improvement, with only 1 patient suffering from significant hypertension. Radiological improvement, defined as reduced T2/fluid-attenuated inversion recovery signal and mass effect, was observed in 50% of patients; however, this did not completely overlap with clinical response. Both early and late radiation necrosis responded equally well to bevacizumab therapy. Overall, bevacizumab was very well tolerated, permitting a reduction of corticosteroid dose and/or duration in the majority of patients. CONCLUSIONS: Bevacizumab appears to be effective and well-tolerated in children as treatment for symptomatic radiation necrosis and warrants more robust study in the context of controlled clinical trials.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.005 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
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 teacher head, 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".