Bibliometric analysis of colchicine in cardiovascular health: trends, key contributors, and global collaborations
Bibliographic record
Abstract
Background/aim: The study aims to systematically analyze the body of literature concerning the effects of colchicine on the cardiovascular system, providing a comprehensive evaluation of the current knowledge and highlighting the clinical and scientific relevance of colchicine in this field. Materials and methods: Data were obtained from the Web of Science (WOS), Scopus, and PubMed databases on March 17, 2025. The search terms included "Colchicine" AND ("Cardiovascular Diseases" OR "Heart Diseases" OR "Coronary Artery Disease" OR "Myocarditis" OR "Pericarditis" OR "Atherosclerosis" OR "Heart Failure" OR "Myocardial Infarction" OR "Ischemic Heart Disease" OR "Acute Coronary Syndrome" OR "Cardiac Arrhythmias" OR "Thrombosis" OR "Stroke"). No restrictions were applied regarding publication date or language, ensuring the comprehensive retrieval of relevant data. Publications were categorized according to their document type and indexes. Additionally, a cooccurrence analysis of key words was conducted using VOSviewer 1.6.18. Results: A total of 425 publications on the effects of colchicine on the cardiovascular system were identified, published between 1976 and 2025. There has been a significant increase in research since 2015. Most publications were in the Cardiac Cardiovascular Systems category. "Colchicine" was the most frequently used key word, appearing 170 times. The USA ranked first in publication count, with 163 studies. The USA, Italy, Canada, and China played significant roles in global research collaboration. Conclusion: This bibliometric analysis demonstrates the increasing importance of colchicine as a therapeutic agent in the management of cardiovascular disease.
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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 | 0.008 | 0.047 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.003 |
| Bibliometrics | 0.127 | 0.184 |
| 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) | 0.006 | 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".