Direct oral anticoagulant versus low molecular weight heparin for the treatment of cancer-associated venous thromboembolism: 2022 updated systematic review and meta-analysis of randomized controlled trials
Bibliographic record
Abstract
International clinical practice guidelines have progressively endorsed direct oral anticoagulants (DOACs) as an alternative to low-molecular-weight heparins (LMWHs) monotherapy for the initial and long-term treatment of cancer-associated thrombosis (CAT). Several new randomized controlled trials (RCTs) have recently reported additional results on the safety and efficacy of DOACs in this setting. We performed an updated meta-analysis of all publicly available data from RCTs comparing DOACs with LMWHs for the treatment of CAT. Six RCTs enrolling 3690 patients with CAT were included. Compared with LMWHs, DOACs significantly decreased the risk of CAT recurrence (RR, 0.67; 95%CI, 0.52-0.85), with a non-significant increase in the risk of major bleeding (RR, 1.17; 95%CI, 0.82-1.67), a significant increase in the risk of clinically relevant nonmajor bleeding (RR 1.66; 95%CI, 1.31-2.09) and no difference in all-cause mortality rates. These results increase the level of certainty of available evidence supporting the use of DOACs as an effective and safe option for the treatment of CAT in selected cancer patients.
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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 | 0.009 | 0.021 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.013 | 0.018 |
| Bibliometrics | 0.005 | 0.005 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.001 |
| Open science | 0.001 | 0.001 |
| Research integrity | 0.002 | 0.002 |
| Insufficient payload (model declined to judge) | 0.005 | 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".