Research trends and global productivity on mechanical ventilation with the impact of COVID-19: a bibliometric analysis in the period 1980-2021
Bibliographic record
Abstract
Aim: Although the number of global studies on mechanical ventilation (MV) therapy, which plays an important role in the life process of patients in the intensive care unit, has increased, there is still no bibliometric research on this subject in the literature. This study, it was aimed to determine trend topics and global productivity by holistically analyzing scientific articles on MV published between 1980 and 2021 using various statistical methods and bibliometric approaches. Material and Method: Articles on MV published between 1980 and 2021 were downloaded from the Web of Science (WoS) database and analyzed using various statistical methods. Spearman's correlation coefficient was used for correlation studies. Network visualization maps were used to identify the most effective studies with global collaborations, trend topics, and citation analysis. Results: The study, which was in the category of 5323 articles out of a total of 10135 publications, was analyzed. The first 3 countries that contributed the most to the literature were the USA (n=1740), France (448), and Canada (386). The most active author was Laurent Brochard (n=50). The top 3 most active institutions were Assistance Publique Hopitaux Paris (224), University of Toronto (216), and League of European Research Universities (169). The top 3 journals that published the most articles were Critical Care Medicine (289), Chest (204), and Intensive Care Medicine (166). Gross Domestic Product (GDP) was highly effective in article productivity (r=0.719, p
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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 | 0.006 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.092 | 0.137 |
| Science and technology studies | 0.001 | 0.001 |
| Scholarly communication | 0.004 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.002 | 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".