Extended treatment of venous thromboembolism: a systematic review and network meta-analysis
Bibliographic record
Abstract
OBJECTIVE: To evaluate efficacy and safety of oral anticoagulant regimens and aspirin for extended venous thromboembolism (VTE) treatment. METHODS: We searched MEDLINE, Embase, CENTRAL and conference proceedings for randomised controlled trials studying vitamin K antagonists (VKAs), direct oral anticoagulants (DOACs) or aspirin for secondary prevention of VTE beyond 3 months. ORs (95% credible intervals) between treatments were estimated using random-effects Bayesian network meta-analysis. RESULTS: Sixteen studies, totaling more than 22 000 patients, were included. Compared with placebo or observation and with aspirin, respectively, the risk of recurrent VTE was lower with standard-intensity VKAs (0.15 (0.08 to 0.24) and 0.23 (0.09 to 0.54)), low-dose factor Xa inhibitors (0.16 (0.06 to 0.38) and 0.25 (0.09 to 0.66)), standard-dose factor Xa inhibitors (0.17 (0.08 to 0.33) and 0.27 (0.11 to 0.65)) and the direct thrombin inhibitor (0.15 (0.04 to 0.37) and 0.23 (0.06 to 0.74)) although the risk of major bleeding was higher with standard-intensity VKAs (4.42 (1.99 to 12.24) and 4.14 (1.17 to 18.86)). Effect estimates were consistent in male patients and those with index pulmonary embolism or with unprovoked VTE and in sensitivity analyses. In addition, compared with placebo or observation, the risk of all-cause mortality was reduced with standard-intensity VKAs (0.44 (0.20 to 0.87)) and low-dose factor Xa inhibitors (0.38 (0.12 to 0.995)). CONCLUSIONS: Standard-intensity VKAs and DOACs are more efficacious than aspirin for extended VTE treatment. Despite a higher risk of major bleeding, standard-intensity VKAs was associated with a lower risk of all-cause mortality. Since overall efficacy and safety of standard-intensity VKAs and DOACs are in equipoise, patient factors, costs and patient preferences should be considered when recommending extending anticoagulation treatment.
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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 | 0.040 |
| Meta-epidemiology (narrow) | 0.003 | 0.001 |
| Meta-epidemiology (broad) | 0.020 | 0.035 |
| Bibliometrics | 0.008 | 0.008 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.002 |
| Open science | 0.002 | 0.002 |
| Research integrity | 0.002 | 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".