Bibliometric Analysis of Non-Vitamin K Antagonist Oral Anticoagulants (NOACS) in the Prevention of Venous Thrombosis and Pulmonary Embolism
Bibliographic record
Abstract
Introduction: Venous thromboembolism (VTE) is a leading cause of cardiovascular-related deaths. Non-vitamin K antagonist oral anticoagulants (NOACs) offer effective therapy without injections or blood monitoring. This bibliometric analysis explores the research on NOACs for preventing VTE and pulmonary embolism. Methods: Literature up to July 20, 2024, was searched in Web of Science Core Collection. Citespace software was used for screening and analysis. Results: In this study, we analyzed 2124 articles and 767 reviews from 11,282 institutions across 528 countries and regions, encompassing 830 publications and 60 research directions. The USA led in publication count, followed by Germany and Canada. Cardiovascular System Cardiology, Hematology, and General Internal Medicine were the top research areas, while THROMBOSIS AND HAEMOSTASIS was the leading journal. From 2004 to 2024, we observed accelerated publication growth, particularly from 2008, highlighting the emergence of NOACs as a major research focus. Key contributors, including Bengt I. Eriksson, and major institutions like Harvard Medical School and University of Amsterdam, played pivotal roles in advancing anticoagulant research. Co-citation and keyword clustering analyses revealed research hotspots in NOACs, cancer-associated venous thromboembolism, stroke prevention, and COVID-19-related thrombotic events, reflecting a shift towards individualized anticoagulation therapy and the growing importance of NOACs in various clinical contexts. Conclusion: The development of NOACs has progressed rapidly, with an increasing number of publications, indicating the lead research in the United States and other Western nations. Comparative studies on the safety and efficacy of NOACs have become a significant focus, shifting from traditional anticoagulants. Pharmacogenetics-guided use of NOACS shows new hope of precision medicine.
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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 | 0.010 | 0.073 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.005 |
| Bibliometrics | 0.153 | 0.204 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.005 | 0.001 |
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".