Efficacy and Safety of Direct Oral Anticoagulants for Risk of Cancer-Associated Venous Thromboembolism
Bibliographic record
Abstract
Efficacy and safety of direct oral anticoagulants (DOACs) for preventing primary and recurrent venous thromboembolism (VTE) in patients with cancer remain unclear. In this study, we conducted a systematic review to summarize the most up-to-date evidence from randomized controlled trials (RCTs). Our primary outcomes included the benefit outcome (VTE) and safety outcome (major bleeding). A random-effects model was used to pool the relative risks (RRs) for data syntheses. The Grading of Recommendations Assessment, Development and Evaluation tool was used to evaluate the quality of the entire body of evidence across studies. We included 11 RCTs with a total of 3741 patients with cancer for analyses. The DOACs were significantly related with a reduced risk of VTE when compared with non-DOACs: RR = 0.77, 95% confidence interval [CI]: 0.61-0.99, P = .04. Nonsignificant trend towards a higher risk of major bleeding was found in DOACs: RR = 1.28 95% CI: 0.81-2.02, P = .29. The quality of the entire body of evidence was graded as moderate for risk of VTE, and low for risk of major bleeding. To summarize, DOACs were found to have a favorable effect on risk of VTE but a nonsignificant higher risk of major bleeding compared with non-DOACs in patients with cancer. The safety effect of DOACs in patients with cancer requires further evaluation in adequately powered and designed studies.
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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 | 0.025 | 0.101 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.007 | 0.012 |
| Bibliometrics | 0.005 | 0.003 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.002 | 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".