Research Trends on Pulmonary Rehabilitation: A Bibliometric Analysis From 2011 to 2020
Bibliographic record
Abstract
Background and Objective: A mounting body of evidence suggests that lung function may deteriorate over time with the development of chronic lung diseases (CRDs). Pulmonary rehabilitation has been proved to improve exercise capacity and quality of life in individuals with CRDs. However, PR remains grossly underutilized all around the world. This study aimed to analyze the research trends on PR over the past 10 years. Methods: The publications related to pulmonary rehabilitation in the Web of Science Core Collection (WoSCC) from 2011 to 2020 were searched. VOSviewer (1.6.15) and CiteSpace Software (5.5.R2) were used to analyze authors and co-cited authors, countries and institutions, journals and co-cited journals, co-cited references, and keywords. Results: A total of 4,521 publications were retrieved between 2011 and 2020, and the number of annual publications on pulmonary rehabilitation has shown an overall upward trend in the past decade. The USA was the most productive country, the University of Toronto from Canada was both the first in publications and citations. Spruit MA was both the most productive author and the one with the highest number of co-citations. The first productive journal was the International Journal of Chronic Obstructive Pulmonary Disease, while the first co-cited journal was the American Journal of Respiratory and Critical Care Medicine. The hot keywords were grouped into three clusters, while "Asthma" and "Respiratory society statement" were determined as the frontier topics. Conclusions: The present study successfully revealed the research status and development trends of pulmonary rehabilitation from 2011 to 2020 by using bibliometric analysis, which may help researchers explore and discover new research directions in the future.
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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 | 0.043 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.123 | 0.187 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.005 | 0.004 |
| 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".