Bibliometric Analysis of Literature on Acute Respiratory Distress Syndrome Treatments Published Between 2000 and 2019
Bibliographic record
Abstract
Abstract Background: Acute respiratory distress syndrome (ARDS) is a common and extensively researched condition, and treatment modalities are continuously being developed and improved. Although the literature on ARDS treatment is vast, there have not been any bibliographic analyses examining trends in this area. We aimed to systematically evaluate the literature on ARDS treatments published between 2000 and 2019, from the perspective of bibliometrics. Methods: Literature retrieval was performed in PubMed and in the Web of Science Core Collection, and analyzed for publication and temporal trends. CiteSpace was used to perform co-occurrence analysis for institutions, and reference co-citation analysis for research topics. Burst keyword detection was used to predict future areas of research interest in the field. Results: A total of 13,933 articles were retrieved. The journal Critical Care Medicine published the largest number of articles (956, 6.86%). The University of Toronto was affiliated with the most publications (574, 4.28%) and had the highest degree of betweenness centrality, indicating extensive inter-institution collaboration. The papers on ARDS treatment published between 2000 and 2019 were grouped into 10 major clusters, 3 of which indicated recent activity (“acute lung injury,” “long-term outcome” and “extracorporeal membrane oxygenation”). Fifteen burst keywords/terms were identified, including extracorporeal membrane oxygenation, meta-analysis and oxidative stress. Conclusions: On the basis of the literature published in the preceding 20 years, the exploration of ARDS treatment is an ongoing concern. Extracorporeal membrane oxygenation was an active focus of research in this field. It and oxidative stress are likely to become major topics of research interest in the near future. Meta-analysis will be a popular method in analyzing the efficacy of ARDS treatments.
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 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 | 0.011 | 0.004 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.004 | 0.001 |
| Bibliometrics | 0.237 | 0.246 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.002 | 0.009 |
| Insufficient payload (model declined to judge) | 0.001 | 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".