Mapping the global research landscape and hotspot of exercise therapy and chronic obstructive pulmonary disease: A bibliometric study based on the web of science database from 2011 to 2020
Bibliographic record
Abstract
Background: The application of exercise therapy (ET) in chronic obstructive pulmonary disease (COPD) is generating increasing clinical efficacy and social-economic value. In this study, research trends, evolutionary processes and hot topics in this field are detailed, as well as predictions of future development directions. Methods: Search for literature in the field of COPD and ET and analyze data to generate knowledge graphs using VOSiewer and CiteSpace software. The time frame for the search was from 2011 to January 2021. Then we extracted full-text key information (such as title, journal category, publication date, author, country and institution, abstract, and keyword) and obtained the co-citation analysis. Use hierarchal clustering analysis software developed by VOSviewer to map common citations, and use Citespace software to plot trend networks. Results: The United States topped the list with 27.91% of the number of articles posted, followed by the UK at 25.44%. Imperial College London was the highest number of article publications in institutions, followed by Maastricht University and the University of Toronto. The Royal Brompton Harefield NHS Foundation Trust was one of many research institutions and currently holds the highest average citations per item (ACI) value, followed by Imperial College London and the University of Leuven. Judging from the number of publications related to ET and COPD, it is mainly published in cell biology, respiratory pulmonary diseases, and rehabilitation experiments study medicine. The European Respiration Journal is the most widely published in this field, followed by the International Journal of Chronic Obstructive Pulmonary Disease and Respiratory Medicine. Conclusion: COPD combined with ET is widely used in clinical practice and is on the rise. A distinctive feature of the field is multidisciplinary integration. Rehabilitation research for COPD involves multidisciplinary collaboration, tissue engineering, and molecular biology mechanism studies to help patients remodel healthy breathing. Multidisciplinary rehabilitation measures provide a solid foundation for advancing clinical efficacy in the field of COPD.
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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 | 0.005 | 0.029 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.135 | 0.228 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| Open science | 0.001 | 0.003 |
| 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".