Direct oral anticoagulant (DOAC) versus low-molecular-weight heparin (LMWH) for treatment of cancer associated thrombosis (CAT): A systematic review and meta-analysis
Bibliographic record
Abstract
INTRODUCTION: It is unclear if direct oral anticoagulants (DOACs) are effective and safe alternatives to low-molecular-weight heparin (LMWHs) for the treatment of cancer-associated venous thromboembolism (VTE). We aim to synthesize existing literature that compared DOACs versus LMWHs in this high-risk population. MATERIALS AND METHODS: We conducted a systematic review using EMBASE, MEDLINE and CENTRAL for all observational studies and randomized controlled trials (RCTs) (PROSPERO: CRD42017080898). Two authors independently reviewed study eligibility, extracted data, and assessed bias. Primary outcomes included 6-month recurrent VTE and major bleeding. Secondary outcomes included clinically relevant non-major bleeding (CRNMB) and mortality. RESULTS: We screened 426 articles, reviewed 25 in full-text, and selected 13 and 2 for qualitative and quantitative synthesis, respectively. Based on a meta-analysis of the 2 RCTs, DOACs had lower 6-month recurrent VTE (42/725) when compared to LMWH (64/727) (RR: 0.65 (0.42-1.01)). However, DOACs had higher major bleeding (40/725) when compared to LMWH (23/727) (RR 1.74 (1.05-2.88)). Similarly, CRNMB was higher (RR 2.31 (0.85-6.28)) for patients receiving DOACs. There was no difference in mortality (RR 1.03 (0.85-1.26)). Observational studies were heterogeneous with high risks of bias but showed recurrent VTE rates consistent with the meta-analysis. CONCLUSIONS: DOACs were more effective than LMWHs to prevent recurrent VTE but were associated with a significantly increased risk of major bleeding as well as a trend toward more CRNMB. The absolute risk differences were small (2-3%) for both primary outcomes and may reflect better compliance with DOACs than LMWHs.
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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 | 0.020 | 0.044 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.024 | 0.039 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| 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".