Opportunities and Challenges in Cardio-Oncology: A Bibliometric Analysis From 2010 to 2022
Bibliographic record
Abstract
Cardio-oncology has grown rapidly worldwide as an emerging interdisciplinary discipline over the past decade. In the present bibliometric review, we employed VOSviewer and Citespace software to describe the literature landscape concerning cardio-oncology from 2010 to 2022. As a result, a total of 1,194 relevant publications were identified in the Web of Science database with an increasing trend. The United States dominated the field during the research period, and Italy, England and Canada had emerged as significant contributors to the study. Ky. Bonnie, Herrmann. Joerg and Fradley. Michael G were the most productive researchers. JACC: CardioOncology was the journal dedicated to the discipline of cardio-oncology and had published the greatest number of papers. Vascular disease and atrial fibrillation have attracted much attention as the main cardiovascular burden. Immune checkpoint inhibitor-specific cardiovascular toxicity, biomarkers and imaging examination together with the prevention of cardio-oncology are potential research hotspots. Notably, basic research is lagging behind, for which more researches are needed to fill the gap. In conclusion, bibliometric analysis provided valuable information for the development of cardio-oncology, which is full of opportunities and 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 | 0.008 | 0.027 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.092 | 0.134 |
| 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) | 0.003 | 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".