Anticoagulation of malignant glioma patients in the era of novel antiangiogenic agents
Bibliographic record
Abstract
PURPOSE OF REVIEW: Venous thromboembolism (VTE) is common in patients with brain tumors. Anticoagulant therapy is feared due to the risk of bleeding, especially intracranial hemorrhage. Emerging treatment approaches targeting angiogenesis, may increase the risk of both thrombosis and bleeding. Recent advances in the cause, prevention, and treatment of VTE in brain tumor patients in the era of antiangiogenic therapy are reviewed. RECENT FINDINGS: About 20-30% of malignant glioma patients develop clinically significant thromboembolism; however, a recent randomized trial suggested increased intracranial bleeding with the use of prophylactic anticoagulation. Antiangiogenic therapies, especially those targeting vascular endothelial growth factor or its receptor, may increase the risk of thrombosis. New data regarding the safety of anticoagulation concurrent with bevacizumab have emerged. SUMMARY: VTE is common perioperatively and throughout the course of brain tumor therapy. Therapeutic anticoagulation followed by secondary anticoagulant thromboprophylaxis is indicated in most patients with DVT or pulmonary embolism, including patients receiving antiangiogenic agents. The role of primary antithrombotic prophylaxis remains unclear.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.002 | 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".