An Analysis of Global Research Trends and Top-Cited Research Articles in Cardio-Oncology
Bibliographic record
Abstract
BACKGROUND: As novel cancer therapies continue to improve patient outcomes, there is an increased need for prevention and management of the cardiovascular side effects of these therapies. For this reason, the field of cardio-oncology has experienced significant scientific growth, particularly during the last decade. This study aims to assess the global publication trends and highlight the top-cited scientific articles related to cardio-oncology. METHODS: A comprehensive bibliometric analysis of multiple scientific databases was performed to characterize global publication trends in cardio-oncology from 1864 to 2020 and to determine the top-cited papers addressing cardio-oncology as a field of study. RESULTS: We identified 1,294 publications with 14,494 citations that describe cardio-oncology as a field. Cardio-oncology was the most prevalent term in the literature and was first mentioned in an article from Italy in 1996. There was no further mention of the term "cardio-oncology" until 2003, and later again in 2008. After 2010, there was a consistent increase in the number of publications and citations in cardio-oncology. Among the top 50 most cited papers, there was a noticeable trend of higher number of review articles (n = 28, 56%, with 3,208 citations), followed by guidelines and position papers (n = 9, 18%, with 2,299 citations) and original research articles (n = 9, 18%, with 1,451 citations). The most common specialty for the senior corresponding authors of the top 50 most cited papers was cardiology (n = 36; 72%), followed by oncology (n = 5; 10%); and the most prevalent countries of origin were the USA (n = 26; 52%), Italy (n = 8; 16%), and Canada (n = 6; 12%). CONCLUSION: Our quantitative analysis of publication trends in the field of cardio-oncology objectively showed the growing scientific interest in the field. To our knowledge, this is the first bibliometric analysis that determined the top 50 most cited articles in the field of cardio-oncology.
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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 | 0.017 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.003 | 0.016 |
| Science and technology studies | 0.000 | 0.002 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.001 | 0.002 |
| 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; a candidate call from one teacher head, 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".