Preventative and curative treatment of venous thromboembolic disease in cancer patients
Bibliographic record
Abstract
Cancer-associated venous thromboembolism (CAT) is common in patients with cancer and associated with significant morbidity and mortality. The incidence of CAT continues to rise, complicating patient care and burdening healthcare systems. Patients with cancer experiencing VTE face poorer prognoses, making prevention and effective management imperative. This narrative review synthesizes evidence on thromboprophylaxis in ambulatory patients with cancer receiving systemic therapy and acute treatment strategies for CAT. Risk assessment models (e.g., Khorana score) aid in identifying high-risk patients who may benefit from thromboprophylaxis. Pharmacological thromboprophylaxis with low molecular weight heparins (LMWHs) and direct oral anticoagulants (DOACs) has been shown to reduce the risk of CAT without significantly increasing the risk of bleeding complications. However, implementation of risk-based strategies remains limited in clinical practice. For acute CAT management, LMWHs have been the standard of care, but DOACs are increasingly favored due to their convenience and efficacy. However, challenges persist, including bleeding risks and drug interactions. Emerging therapies targeting Factor XI inhibitors present promising alternatives, potentially addressing current limitations in anticoagulation management for CAT.
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 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) | 0.002 | 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 itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, 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".