Bibliometric Analysis of the Results of Cardio‐Oncology Research
Bibliographic record
Abstract
Objective . To analyze the development of cardio‐oncology, summarize the research achievements, and provide proposals for its future research. Methods . The web of science database was used to search for “cardio‐oncology” and “oncocardiology” related articles from the beginning of the database (1970) to April 5, 2019. Excel 2016 and Cytoscape were used to analyze the trend of cardio‐oncology research. Results . A total of 356 articles were obtained. The number of articles has grown rapidly in recent years. Cardiac injury caused by tumor therapy was a research hotspot ( n = 107). Researchers paid more attention to the prevention and treatment of cardiotoxicity ( n = 54). Experimental researches were a small part of all studies ( n = 72), mainly focusing on the study of cancer drugs’ cardiac injury, test indicators of cardiotoxicity, and preventive drugs. The United States ( n = 156.25), Italy ( n = 48.5), and Canada ( n = 23.5) published the most articles, making a great contribution to the development of cardio‐oncology. Conclusions . Cardio‐oncology has been developing rapidly and receiving a large amount of research efforts in recent years. Most articles on cardio‐oncology were published by the authors from the United States (44%) and Italy (17%), while other countries need to pay more attention to cardio‐oncology. As an independent discipline, cardio‐oncology is certainly in need of significant progress, but it has formed a basic framework, which has obtained many leading theories and meaningful achievements in diagnostic criteria, diagnostic methods, prevention and treatment, mechanism research, and influencing factor. Cardiac injury of tumor drugs has always been a research hotspot in this discipline, and there is still a lot of research space. The research about detection methods of cardiotoxicity and preventive drugs is gradually increasing. Basic research lags behind, and many mechanisms are still unclear.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | low |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
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 | 0.002 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.011 | 0.058 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
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".