Direct Oral Anticoagulants in the Management of Cancer-Associated Venous Thromboembolism: A Comprehensive Review
Bibliographic record
Abstract
Venous thromboembolism (VTE) is reportedly the second common cause of death among cancer patients. Management of VTE with low molecular weight heparins (LMWH) and Vitamin K antagonists (VKA) is associated with significant clinical challenges that led to the emergence of direct oral anticoagulants (DOAC) as an alternative for the management of cancer-associated thrombosis (CAT). The objective of this review is to provide an updated review of the completed and upcoming randomized controlled clinical trials (RCT) comparing the safety and efficacy of DOAC versus LMWH in the management of CAT in adults. A comprehensive literature survey was conducted till 7th January 2021 to review the completed randomized controlled clinical trials (RCT) comparing the safety and efficacy of DOAC versus LMWH in the management of CAT in adults. In order to search for upcoming trials, the Cochrane library and clinicaltrials.org databases were surveyed until the same period. The author found four completed RCT of DOAC in the management of CAT. Apart from these, there were another four RCT that are either ongoing or are yet to publish data. DOAC were reported to be noninferior to LMWH in the treatment of cancer-associated thrombosis in cancer patients with low bleeding risk and without gastrointestinal or gastrourinary cancers. Among patients with gastrointestinal or gastrourinary cancers and VTE, LMWH are preferred.
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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) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.004 | 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".