The Application of Angiotensin Receptor Neprilysin Inhibitor in Cardiovascular Diseases: A Bibliometric Review From 2000 to 2022
Bibliographic record
Abstract
Cardiovascular disease (CVD) has become a huge challenge for the global public health system due to its high morbidity, mortality and severe economic burden. In recent years, angiotensin receptor neprilysin inhibitor (ARNI), a new class of drugs, has shown good therapeutic effects on CVD patients in several clinical studies, reducing the morbidity and mortality of CVD patients. In this study, we retrieved publications on ARNI research in the cardiovascular field from the Web of Science core collection and analyzed the annual output, spatial and temporal distribution, institutions and authors, core journals, keywords and co-cited literature based on CiteSpace. As a result, 604 publications were retrieved, and the number of annual publications generally increased year by year, with the largest number of articles. The analysis of the co-occurrence of output countries and authors showed that a few developed countries such as the United States, Canada, and United Kingdom are the most active in this field, forming academic groups represented by John Joseph Valentine McMurray and Scott D. Solomon, and New England Journal of Medicine, Cirulation, and Journal of the American College of Cardiology are the most popular journals in the field, with research hotspots focused on ARNI in the treatment of total ejection fraction heart failure, hypertension and its target organ damage, with the potential for future benefit throughout the cardiovascular event chain as research progresses. This study reveals the prospective application of ARNI in the cardiovascular field and the research hotspots, providing broader and deeper guidance for its use in the clinic, which is beneficial to improve the treatment and prognosis of CVD patients.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.005 | 0.001 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.009 | 0.005 |
| Bibliometrics | 0.015 | 0.053 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 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; both teacher heads agree on what is shown here.
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".