A bibliometric analysis of acute respiratory distress syndrome (ARDS) research from 2010 to 2019
Bibliographic record
Abstract
BACKGROUND: Thousands of papers on acute respiratory distress syndrome (ARDS) have been published in the last decade. This study aimed to evaluate the research hotspots and future trends in ARDS research using bibliometric analysis. METHODS: All relevant literature on ARDS published between 2010 and 2019 was retrieved from the Web of Science Core Collection database, and the retrieval strategy was TS = (ARDS OR acute respiratory distress syndrome). Bibliometric analysis was conducted using VOSviewer and the online bibliometric analysis platform based on retrieved data. Bibliographic Item Co-occurrence Matrix Builder (BICOMB) and gCLUTO software were used to evaluate and visualize the results, and to explore the hotspots in the field of ARDS. RESULTS: A total of 9,858 ARDS research articles dated between 2010 and 2019 were included. The dominant position of the United States in global ARDS research throughout this 10-year period was evident, and it was also the country most frequently involved in international cooperation. The University of Toronto was the most productive institution and a leader in research collaboration. Critical Care Medicine was the most productive journal in terms of the number of publications on ARDS. Further, Matthay MA, Pelosi P, Slutsky AS, and Thompson BT all made significant contributions to ARDS research. A total of 37 most frequent keywords were identified and belonged to 5 hotspots: (I) adult and pediatric ARDS; (II) life-support monitoring parameters and therapy in severe patients with ARDS; (III) molecular mechanisms of acute lung injury; (IV) influenza-related pneumonia; and (V) severe complications of ARDS. Also, in the last 5 years, the keywords "biomarkers", "pathway", "NF-κB", "epidemiology", "life-support", and "ECMO" began to appear in the ARDS research field. CONCLUSIONS: In the decade from 2010 to 2019, the United States was a global leader in ARDS research, and hotspots included epidemiology, mechanisms, monitoring parameters, and therapy, especially mechanical ventilation. Our results suggest that the mechanisms of ARDS and novel life-support therapies will remain research hotspots in the future. International collaboration is also expected to widen and deepen in the field of ARDS research.
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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 | 0.010 | 0.054 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.003 | 0.004 |
| Bibliometrics | 0.200 | 0.246 |
| 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) | 0.004 | 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".