Bibliometric analysis of cardiac rehabilitation for coronary disease research trends based on Web of Science
Bibliographic record
Abstract
Objective To perform bibliometric analysis of research literature in the field of cardiac rehabilitation of coronary disease, in order to accurately grasp the research situation in the field in the world, and to provide references for further in-depth research. Methods The distribution characteristics, research fronts, and research hotspots for the cardiac rehabilitation of coronary disease were analyzed based on researching systematically of documents Web of Science before December 31st, 2017, with the aid of CiteSpace software, using co-citation analysis and co-word analysis to analyze. Results A total of 2 560 articles were retrieved. The institutions and authors in United States, Canada and other European and American countries had a relatively high level of research in the field of cardiac rehabilitation of coronary disease. Gender-tailored randomized clinical trials and prospective studies were the research frontiers in this field. Randomized controlled trials and meta-analysis were commonly used research methods in this field. Exercise was the main research content of cardiac rehabilitation programs for patients with coronary disease. Mortality, quality of life, risk factors, and depression were currently the main indicators for assessing the effectiveness of cardiac rehabilitation programs in these patients. Conclusions The current research in the field of cardiac rehabilitation of coronary disease in China has gradually started. In the future, we can explore new entry points and breakthroughs in combination with current international research frontiers and research hotsopts. Key words: Bibliometric; Cardiac rehabilitation; Coronary disease
Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.
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 | 0.005 | 0.002 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.105 | 0.203 |
| Science and technology studies | 0.000 | 0.002 |
| 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".