Top 100 Influential Articles in the Field of ACE Inhibitors in Preventing Cardiac Remodeling: A Bibliometric Analysis
Bibliographic record
Abstract
Background: Cardiac remodeling can lead to poor outcomes like dangerous arrhythmias, ventricular dysfunction, and heart failure. Though decades of research have been conducted on cardiac remodeling and Angiotensin-converting enzymes, a comprehensive analysis using bibliometrics has yet to be performed. Methodology: This study aimed to do a citation analysis of the hundred frequently cited articles on ACE inhibitors for the prevention of cardiac remodeling using the Scopus database. The Scopus database was searched for relevant literature by analyzing the titles, keywords, and abstracts of papers. The studies discovered were all about ACE inhibitors. The abstracts of each article were examined to determine their significance and appropriateness for the enclosure. The final selection included 100 articles, and citation analysis was performed using manual screening and Scopus. Results: The trend of total citations increased sharply beginning in 1987, peaking around 1992, then gradually declining with occasional fluctuations until a significant uptick was seen in 2002. The articles were published between 1978 and 2022, with the highest number of articles published between 1999 and 2003. The articles originated in 24 countries, with the United States having the most representation. The articles were published in 11 different journals, with the first four accounting for more than three-quarters of the total. The top institution, with 14 articles, was Brigham and Women's Hospital. Additionally, the majority of the authors were male and of white origin. The study discovered that while the majority of authors (335/388) did not have conflicts of interest, a significant minority did, primarily among higher-ranking authors. Conclusion: Our research aims to give a comprehensive understanding of the current studies and investigations regarding the use of Ace inhibitors in cardiovascular remodeling, in order to better inform future research efforts.
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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 | 0.005 | 0.039 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.121 | 0.119 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".