Pulmonary Rehabilitation in Patients with Mechanical Ventilation: Bibliometric Analysis (Preprint)
Bibliographic record
Abstract
BACKGROUND Pulmonary rehabilitation has been widely used in patients with mechanical ventilation. However, in-depth bibliometric studies that measure and analyze scholarly publications worldwide are still rare. OBJECTIVE This study aims to characterize the publications in terms of countries, institutions, authors, journals, and collaboration relationships, and analyze the research trends of pulmonary rehabilitation in patients with mechanical ventilation. METHODS Publications regarding pulmonary rehabilitation in patients with mechanical ventilation were retrieved from the Web of Science core collection. To describe the contributions of the writers, journals, institutions, and nations, Microsoft Excel 2021 and VOSviewer were used. The trends, hot spots, and knowledge networks were analyzed by Citespace and VOSviewer. RESULTS A total of 968 articles and reviews between 2000 and 2022 were included. The number of annual publications grew steadily until 2019, after that, it got a dramatical increase in 2020 and 2022. The USA, Canada, and Italy were playing leading roles in this field. The top two major institutions with a larger number of publications were Queens University and the University of British Columbia. The author with the most output and citations was O'Donnell DE. The three fruitful journals were Journal of Applied Physiology, Respiratory Care, and Chest. Research hotspots have shifted over time in the following categories: the function of PR and targeted patients, which can be observed from the keyword analysis. CONCLUSIONS Research on pulmonary rehabilitation in patients with mechanical ventilation has progressed significantly over the past two decades. The United States, in particular, has made important contributions to this field. The research hotspot is gradually shifting in both outcomes and targeted patients of pulmonary rehabilitation.
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 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 | 0.036 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.002 |
| Bibliometrics | 0.071 | 0.119 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.004 | 0.002 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.000 | 0.000 |
| 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".