CLIN-ONGOING CLINICAL TRIALS
Bibliographic record
Abstract
INTRODUCTION: Bevacizumab has been reported to be an effective treatment for symptomatic radiation necrosis and to decrease focal edema around areas of radiation necrosis.We report our preliminary results and ongoing clinical trial of bevacizumab treatment for radiation necrosis.METHODS: Thirteen patients with symptomatic radiation necrosis were treated with bevacizumab.Radiation necrosis was diagnosed according to the patients' clinical courses, magnetic resonance images, and fluoridelabeled boronophenylalanine-positron emission tomography (F-BPA-PET).Lesion/normal (L/N) ratios less than 2.0 and 2.5 on F-BPA-PET were defined as absolute and relative indications for bevacizumab treatment, respectively.The patients were treated with bevacizumab at a dose of 5 mg/ kg every 2 weeks, 6 cycles in total.RESULTS: Two patients were excluded from analysis because of adverse events.Eleven patients underwent 3 to 6 cycles of bevacizumab treatment.The median rate of the reduction in peri-lesional edema was 65.5% (range: 2.0% to 81.0%).The Karnofsky performance status (KPS) improved in 6 patients after bevacizumab treatment, and in 5 patients the status did not change.The L/N ratio on F-BPA-PET (P ¼ 0.0084) and the improvement of KPS after bevacizumab (P ¼ 0.0228) were significantly associated with the reduction rate of peri-lesional edema after bevacizumab treatment.CONCLUSION: Bevacizumab is a very effective treatment for radiation necrosis, irrespective of the original tumor histology.F-BPA-PET could be useful for diagnosing radiation necrosis and for making the decision as to whether or not to treat symptomatic radiation necrosis with bevacizumab.The clinical trial "Intra-venous administration of bevacizumab for the treatment of radiation necrosis in the brain" has been approved as Investigational Medical Care System by the Japanese Ministry of Health, Labour and Welfare.This trial has been ongoing since April, 2011.
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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 | 0.013 | 0.018 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.006 | 0.003 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.005 | 0.005 |
| Insufficient payload (model declined to judge) | 0.247 | 0.113 |
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".