Effectiveness and safety of non-vitamin K antagonist oral anticoagulant in the treatment of patients with morbid obesity or high body weight with venous thromboembolism: A meta-analysis
Bibliographic record
Abstract
BACKGROUND: Venous thromboembolism (VTE) poses a significant health risk to patients with morbid obesity or high body weight. Non-vitamin K antagonist oral anticoagulants (NOACs) are emerging treatments, but their effectiveness and safety compared with vitamin K antagonists (VKAs) in this population are yet to be thoroughly studied. METHODS: We conducted a systematic review and meta-analysis, adhering to the Preferred Reporting Items for Systematic Reviews and Meta-Analyses guidelines. Four electronic databases were searched for relevant studies comparing the efficacy and safety of NOACs and VKAs in treating patients with VTE with a body mass index > 40 kg/m2 or body weight > 120 kg. Eligible studies were scored for quality using the Newcastle-Ottawa Scale. RESULTS: Thirteen studies were included. The meta-analysis results showed that compared to VKAs, NOACs significantly decreased the risk of VTE occurrence (odds ratio = 0.72, 95% CI: 0.57-0.91, I2 = 0%, P < .001) and were associated with a lower risk of bleeding (odds ratio = 0.74, 95% CI: 0.58-0.95, I2 = 0%, P < .05). Subgroup analysis showed that in the cancer patient subgroup, both risks of VTE occurrence and bleeding were lower in the NOAC group than in the VKA group. In patients without cancer, the risk of VTE was significantly lower in the NOAC group. CONCLUSION: NOACs appear to be more effective and safer than VKAs in patients with morbid obesity or a high body weight with VTE. However, further large-scale randomized controlled trials are required to confirm these findings.
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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 | 0.025 |
| Meta-epidemiology (narrow) | 0.003 | 0.002 |
| Meta-epidemiology (broad) | 0.021 | 0.070 |
| Bibliometrics | 0.006 | 0.006 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.003 | 0.001 |
| Open science | 0.002 | 0.001 |
| Research integrity | 0.003 | 0.002 |
| Insufficient payload (model declined to judge) | 0.003 | 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".