Safe and effective treatment of venous Thromboembolism associated with Cancer: focus on direct Oral Anticoagulants in Asian patients
Bibliographic record
Abstract
Abstract Cancer-associated thrombosis (CAT) poses a significant disease burden and the incidence in Asian populations is increasing. Anticoagulation is the cornerstone of treatment, but can be challenging due to the high bleeding risk in some cancers and the high risk of recurrent venous thromboembolism (VTE) in patients with malignancies. Direct oral anticoagulants (DOACs) are well established as first-choice treatments for VTE in non-cancer patients, offering a more convenient and less invasive treatment option than low-molecular-weight heparin (LMWH). Asian patients have exhibited comparable efficacy and safety outcomes with other races in trials of DOACs for VTE in the general population. Although no specific data are available in Asian patients with CAT, results from randomized controlled trials of apixaban, edoxaban, or rivaroxaban versus the LMWH, dalteparin, indicate that DOACs are a reasonable alternative to LMWH for anticoagulation in Asian patients with CAT. This is further supported by analyses of real-world data in Asian populations demonstrating the efficacy and safety of DOACs in Asian patients with CAT. Apixaban, edoxaban, or rivaroxaban are recommended in the most recently updated international guidelines as first-line therapy for CAT in patients without gastrointestinal or genitourinary cancers and at low risk of bleeding. An increased risk of major gastrointestinal bleeding was evident with edoxaban or rivaroxaban, but not apixaban, versus dalteparin in the clinical trials, suggesting that apixaban could be a safe alternative to LMWH in patients with gastrointestinal malignancies. Determining the optimal anticoagulant therapy for patients with CAT requires careful consideration of bleeding risk, tumor type, renal function, drug–drug interactions, financial costs, and patients' needs and preferences.
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 | 0.001 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.001 |
| Bibliometrics | 0.000 | 0.001 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".