Risk of Adverse Vascular Events in Newly Diagnosed Glioblastoma Multiforme Patients Treated with Bevacizumab: a Systematic Review and Meta-Analysis
Bibliographic record
Abstract
Previous evidence suggests that the humanized anti-VEGF antibody bevacizumab increases thrombosis risk in glioma patients. Here, we comprehensively assessed the risk of adverse vascular events in adult glioma patients receiving bevacizumab therapy. Systematic searches of MEDLINE, EMBASE, and the Cochrane Library were conducted to find prospective phase II/III clinical trials on adult bevacizumab-treated glioma patients and non-bevacizumab-treated controls that reported data on adverse vascular events. Four high-quality trials were finally included in the systematic review, scoring greater than or equal to 7/8 on the Newcastle-Ottawa Scale. Three trials provided sufficient data for four meta-analytical comparisons between bevacizumab-treated and control groups of newly diagnosed glioblastoma multiforme (GBM) patients: all-cause discontinuation, thrombocytopenia, deep vein thrombosis (DVT), and pulmonary embolism. None of these adverse outcomes were found to be significantly different between bevacizumab-treated and control groups (P > 0.05); however, there was a trend toward significance with regard to bevacizumab therapy and the risk of pulmonary embolism (P = 0.07). As there was a trend toward significance with regard to bevacizumab therapy and the risk of pulmonary embolism, anticoagulation may be advisable in certain newly diagnosed adult GBM patients who display a history of thromboembolism and/or more serious risk factors for thromboembolic events.
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.007 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.011 | 0.022 |
| Bibliometrics | 0.006 | 0.007 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".