The Top 100 Cited Articles Focusing on Acute Lung Injury and ARDS: Bibliometric and Visualization Analyses
Bibliographic record
Abstract
BACKGROUND: In recent years, acute lung injury (ALI) and ARDS have emerged as critical health concerns, drawing considerable attention from clinicians. The volume of published articles on ALI/ARDS is on the rise, indicating the expanding research interest in this field. However, the precise quantity and quality of studies on ALI/ARDS remain unclear. Consequently, we employed bibliometric and visual techniques to comprehensively analyze the patterns and focal points of these articles. METHODS: To investigate the characteristics of highly referenced papers on ALI/ARDS and offer insights into the progress and advancements in research on ALI/ARDS, we conducted a comprehensive search in the core Web of Science database for cited articles using the terms "ALI," "acute lung injury," "ARDS," or "acute respiratory distress syndrome." A total of 60,282 citations were retrieved by narrowing the scope to reviews, articles, and publications in English. From the obtained citations, we selected materials for analysis from the top 100 articles with the highest number of citations. Subsequently, the articles were visualized and analyzed using VOSviewer, CiteSpace, and bibliometric techniques. This analysis focused on identifying trends related to authors, journals, countries, institutions, collaborative networks, key words, and other relevant factors in the field of ALI/ARDS research. RESULTS: (no. = 14). Among the 29 countries represented in the top 100 cited articles, the United States (no. = 51) emerged as the leading country in the number of publications, followed by Canada (no. = 19) (there was some overlap in paper output between countries due to co-publication). The 3 predominant keywords identified in studies within the ALI/ARDS domain were ALI, mechanical ventilation, and PEEP. CONCLUSIONS: This study provides a historical perspective on the scientific advancements in ALI/ARDS research, highlighting the need for further investigation and development in specific areas within the field. Bibliometric analyses revealed that the United States is the predominant force in the field of ALI/ARDS, contributing significantly to its development. Through an examination of highly cited papers on ALI/ARDS, we have identified global research trends, assessed the quality of studies, and identified hot topics in the field of ALI/ARDS.
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How this classification was reachedexpand
Direct model labels (unvalidated)
Per-model category and study-design labels from the labeling rounds. They are machine output, unvalidated, and the disagreement between models ships as data. No study design here is MEDLINE-validated yet.
| Model arm | Categories | Study design | Confidence |
|---|---|---|---|
| gemma | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Observational | high |
| gpt | Bibliometrics Domain: not available · Genre: Empirical About the Canadian research system: no · About a Canadian topic: no | Other design | high |
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 | 0.006 | 0.010 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.293 | 0.609 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.009 | 0.001 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| 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, unvalidatedLabeled directly by 2 models reading the full record.
The models disagree on parts of this classification; every voice is preserved in the section at the end of the page.
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".