Global research trends in influenza-associated acute lung injury/acute respiratory distress syndrome: a bibliometric analysis from 2010 to 2024
Bibliographic record
Abstract
Background: Acute lung injury (ALI) and its more severe form, acute respiratory distress syndrome (ARDS), are common critical conditions that pose significant threats to patients' lives and health. In recent years, numerous studies have focused on influenza-associated ALI/ARDS due to the emergence of novel and unexpected infectious diseases. However, no bibliometric analysis has yet been conducted to provide a comprehensive overview of research in this specific area. This study aims to explore the research hotspots and development trends in the field of influenza-associated ALI/ARDS through bibliometric analysis. Methods: Publications on influenza and ALI/ARDS research, published between 2010 and 2024, were retrieved from the Web of Science Core Collection (WoSCC) and subjected to bibliometric analysis using tools such as CiteSpace, VOSviewer, and Microsoft Excel. Results: This study analyzed 1,814 articles on influenza-associated ALI/ARDS, involving 11,403 researchers from 6,554 institutions across 411 countries/regions. The United States had the highest number of publications, while the University of Toronto was the most productive institution. Professor Alain Combes of Sorbonne University, France, was the leading author. Furthermore, several journals, notably Frontiers in Immunology, have significantly influenced research in this field. Future research should focus on elucidating the pathogenesis of ALI/ARDS in response to new influenza outbreaks and investigating therapeutic strategies targeting the various underlying mechanisms. Conclusions: This study provides an overview of the current status and hot issues in influenza-associated ALI/ARDS research, highlights emerging trends for future development, and offers guidance on potential research directions for scholars in this field.
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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 | 0.041 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.003 |
| Bibliometrics | 0.144 | 0.240 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| 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".