Characterization of Global Research Trends and Prospects on Prone Positioning in Respiratory Failure: Bibliometric Analysis
Bibliographic record
Abstract
Background: Prone positioning has emerged as a crucial intervention in managing acute respiratory failure, especially in acute respiratory distress syndrome and patients with COVID-19. Given the increasing interest in this field, it is important to characterize global research trends and key contributors to identify future research directions. Objective: This study aimed to analyze global research trends, collaboration networks, and research hotspots related to prone positioning in respiratory failure through a comprehensive bibliometric analysis. Methods: Bibliometric analyses were conducted using CiteSpace and Biblioshiny software on publications up to December 31, 2023, from the Web of Science Core Collection, focusing on prone positioning in respiratory failure. Results: A total of 1263 research articles were identified, published in 50 countries by numerous institutions. The United States, France, and Germany contributed the most publications, with the United States producing 21.9% (275/1263) of the total. Key authors such as Claude Guerin and Luciano Gattinoni were identified as major contributors to the field. Keyword co-occurrence analysis revealed the dynamic nature of prone positioning research in respiratory failure. It highlighted protective ventilation and COVID-19-related acute respiratory distress syndrome as emerging hotspots, indicating a shift in focus during the pandemic. Conclusions: This study revealed a rapidly growing body of literature on prone positioning in respiratory failure, especially in the context of COVID-19. The findings underscore the importance of further multicenter clinical trials to validate current practices and refine treatment protocols. In addition, the application of prone positioning in non-intubated patients represents a potential future research direction.
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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 | 0.012 | 0.007 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.087 | 0.125 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.002 |
| 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".