BIOM-57. ANTITHROMBOTIC THERAPY IN PATIENTS WITH BRAIN TUMORS
Bibliographic record
Abstract
Abstract IMPORTANCE Managing antithrombotic therapy (ATT) in patients with brain tumors (BTs) presents a critical clinical challenge due to the dual risks of thrombosis and hemorrhage. Up to 24% of patients with glioblastoma and 20% with brain metastases develop venous thromboembolism (VTE), while up to 13% experience spontaneous intratumoral hemorrhage (ITH) — a potentially fatal complication, especially in anticoagulated patients. Despite improved cancer survival and thereby increasing incidence of BTs, guidelines for ATT in patients with BTs remain limited and vague. OBSERVATIONS A comprehensive literature review demonstrated that hemorrhagic risk varies widely by tumor type; melanoma and renal cell carcinoma metastases are highest with ITH rates up to 40–46%, but ATT does not increase risk. Risk is further elevated by anti-VEGF therapy, surgery, and some systemic treatments. Recent data suggest direct oral anticoagulants (DOACs) may be safer than low molecular weight heparin (LMWH) in patients with glioma, but standard hemorrhage risk tools (e.g., PANWARDS, Khorana score) lack relevance for this population. National guidelines are inconsistent, especially in patients with brain tumors or prior ITH. Preliminary studies indicate that ATT resumption 4–8 weeks after ITH may be safe, but data are retrospective and observational. CONCLUSIONS AND RELEVANCE There is a pressing need for precision-based ATT strategies in patients with BT, particularly those recovering from ITH. Multidisciplinary collaboration and validation of BT-specific predictors of ITH are essential to guide evidence-based care and inform future guideline development. Our ongoing investigation combines institutional tertiary care center retrospective review to 1) identify predictors of ITH in patients with BT, with an international multidisciplinary survey to 2) assess variation in ATT practices following ITH in order to 3) develop risk stratification tools incorporating tumor biology, location, treatment history, and molecular markers to aid ATT decision-making in patients with brain tumors.
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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 | 0.001 | 0.003 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.001 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.006 | 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".