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
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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 | 0.011 | 0.069 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.004 | 0.004 |
| Bibliometrics | 0.260 | 0.261 |
| Science and technology studies | 0.002 | 0.001 |
| Scholarly communication | 0.006 | 0.005 |
| Open science | 0.001 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.007 | 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".