Thromboembolic disease in patients with high-grade glioma
Why this work is in the frame
A frame that forgets how it found something cannot be audited. These are the routes that admitted this work.
Bibliographic record
Abstract
Venous thromboembolism (VTE) is common throughout the course of disease in high-grade glioma (HGG). The interactions between the coagulation cascade, endothelium, and regulation of angiogenesis are complex and drive glioblastoma growth and invasion. We reviewed the incidence of VTE in HGG, the biology of the coagulome as related to glioblastoma progression, prevention and treatment of thrombosis, and the putative role of anticoagulants as anti-cancer therapy. VTE can be significantly reduced during the postoperative period with adherence to the use of mechanical and medical thromboprophylaxis. Activation of the coagulation cascade occurs throughout the course of disease because of a variety of complex interactions, including tumor hypoxia, upregulation of VEGR expression, and increases in both tumor cell-specific tissue factor (TF) expression and inducible TF expression in numerous intrinsic regulatory pathways. Long-term anticoagulation to prevent VTE is an attractive therapy; however, the therapeutic window is narrow and current data do not support its routine use. Most patients with proven symptomatic VTE can be safely anticoagulated, including those receiving anti-VEGF therapy, such as bevacizumab. Initial therapy should include low molecular weight heparin (LMWH), and protracted anticoagulant treatment, perhaps indefinitely, is indicated for patients with HGG because of the ongoing risk of thrombosis. A variety of coagulation- and tumor-related proteins, such as TF and circulating microparticles, may serve as potential disease-specific biomarkers in relation to disease recurrence, monitoring of therapy, and as potential therapeutic targets.
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.
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 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 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 it