Bibliometric analysis of treatment modalities in calcific aortic valve stenosis
Bibliographic record
Abstract
Background: Calcific aortic valve stenosis (CAVS) is a common cardiovascular condition associated with significant adverse events and high mortality rates. Unfortunately, there are currently no effective pharmacological treatments to halt or prevent its progression. Through our analysis of global trends and treatment strategies, we have identified valuable insights and promising therapeutic possibilities. Additionally, by utilizing bibliometric and visualization techniques, we provide a comprehensive overview of the current research landscape in this field. Method: According to our design idea, we used the Web of Science database to select publications on aortic stenosis and related treatments. Through our VOSviewer and CiteSpace analysis, a total of 787 articles have been analyzed by September 2024. We also summarize and explore the most prolific authors, the most prolific countries, and the journals and institutions that publish the most articles. Results: A visual analysis of the collected articles reveals that Canada and the United States have the highest publication volumes in this field. Among institutions, Harvard University in the U.S. leads in publication count, followed by Laval University in Canada and the University of California in the U.S. The top three research hotspots are stenosis, calcification, and progression. The journal with the highest number of publications in this area is Frontiers in Cardiovascular Medicine, followed by Catheterization and Cardiovascular Interventions and Arteriosclerosis, Thrombosis and Vascular Biology. Furthermore, research on CAVS treatment spans various directions and focuses, including therapeutic approaches, pathogenesis, and diagnostic methods. Conclusion: Research into CAVS treatment has advanced significantly over the years. While interventional and surgical valve replacement remains the mainstay treatments for aortic stenosis, they are insufficient to fully meet the needs of the patient. Emerging priorities now focus on improving diagnostics, exploring innovative therapies, uncovering disease mechanisms, and developing novel drugs. These findings highlight the evolving demands in this field and underscore the need for continued research to address these challenges.
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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 | 0.007 | 0.062 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.143 | 0.164 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".