Extended anticoagulation for the secondary prevention of venous thromboembolic events: An updated network meta-analysis
Bibliographic record
Abstract
BACKGROUND: Extended treatment is preconized in a significant proportion of patients with unprovoked venous thromboembolism (VTE). However, limited direct/indirect comparisons are available to appropriately weight the benefit/risk ratio of the diverse treatments available. We aimed to compare the rate of symptomatic recurrent VTE and major bleeding (MB), the net clinical benefit (VTE+MB) and death on vitamin-K antagonist (VKA), direct oral anticoagulants (DOAC) and antiplatelet drugs for extended anticoagulation. METHODS: A systematic literature search through September 2018 identified randomized trials studying these pharmacologic therapies for extended anticoagulation following VTE. Treatment effects were calculated using network meta-analysis with frequentist fixed-effects model. RESULTS: 18 trials (18,221 patients) were included in the analysis. All treatments reduced the risk of recurrence compared to placebo/observation. Nonetheless, VKA (RR 0.22; 95%CI 0.13-0.39) and DOAC (RRs ranging from 0.25-0.32; 95%CI ranging from 0.13-0.52) were more effective than aspirin, whereas low-dose VKA was less effective than standard-dose VKA (RR 2.47; 95%CI 1.34-4.55). The efficacy of DOAC was globally comparable to standard-adjusted dose VKA. Low- (RR 3.13; 95%CI 1.37-7.16) and standard-dose (RR 3.23; 95%CI 1.16-8.99) VKA also increased the risk of MB, which was not the case for any DOAC. Low-dose VKA and low-dose DOAC had similar effects on MB compared to standard-doses. Although there was a trend for reduced MB and enhanced net clinical benefit for DOAC compared to VKA, this was not statistically significant. The specific anticoagulant therapies had no significant effects on deaths. CONCLUSION: Standard-dose VKA and low/standard-dose DOAC share similar effects on VTE recurrence and MB, whereas aspirin and low-dose VKA were associated with lower benefit/risk ratio.
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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 | 0.013 | 0.027 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.012 | 0.042 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.002 | 0.002 |
| Open science | 0.002 | 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".