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 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) | 0.000 | 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 | 0.001 |
| Insufficient payload (model declined to judge) | 0.001 | 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 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".