[Progress of intensive care unit delirium research from 2010 to 2020: analysis based on knowledge visualization].
Bibliographic record
Abstract
OBJECTIVE: To explored the progress of intensive care unit (ICU) delirium between 2010 and 2020 based on knowledge visualization analysis. METHODS: The literatures related to ICU delirium included in Web of Sciences (WOS) and China National Knowledge Infrastructure (CNKI) databases from 2010 to 2020 were collected. A bibliometric analysis was performed. The growth trend was showed by Excel 2019 software. The information about country, institution and author were extracted by VOSviewer 1.6.15 for generating cooperative network, to find the main research power and each cooperative relation. At the same time, Citespace 5.0.R1 was used to analyze those high frequency keywords and bursting keywords and build the map of co-citation reference, in order to explore the evolution of research in the field of ICU delirium and the hotspots about this field in recent 10 years. RESULTS: A total of 1 102 Chinese journal articles and 2 422 English "Articles" or "Reviews" from 2010 to 2020 were collected preliminarily, and the number of published literatures increased steadily. In the respect of quality, the impact factors of most articles were concentrated between 2 and 3, and the literatures with impact factor over 5 accounted for 27.9% (337/1 209). According to the knowledge visualization analysis, the United States published most of the related articles (total 1 152) in this field, while the England and Canada ranked second and third respectively, totaling 220 and 204. In terms of the distribution of research institutions, the Vanderbilt University School of Medicine was not only far ahead in the number of publication (n = 149), but more importantly, top three high-impact authors located in this institution. The amount of domestic publications was lower than developed countries, however, the burst index, which reflected the sudden increase, ranked first (7.09), suggesting that the interest and investment of Chinese researchers was increasing recently. The most productive institution in China was Capital Medical University School of Nursing with totaling 23 articles. Wu Ying, who published most Chinese papers (n = 14), belongs to this institution. However, it was a pity that there was no large scientific community be constructed in China, and the cooperation between institutions was deficient. By generating the co-occuring keyword mapping, the research hotpots mainly focused on the prevention, treatment and prevention of delirium in mechanically ventilated patients, the effect of dexmedetomidine and exploring the risk factor of ICU delirium. Finally, the results of co-citation reference analysis showed that Cluster 4 (risk assessment) was still in the process of development, in hence it was the frontier in this domain. CONCLUSIONS: There was a big gap between China and leading countries in the field of ICU delirium research. The main research power was located in the United States, and the trending of future studies mainly focus on delirium-related risk assessment.
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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 | 0.005 | 0.021 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.002 |
| Bibliometrics | 0.039 | 0.040 |
| Science and technology studies | 0.001 | 0.000 |
| Scholarly communication | 0.003 | 0.003 |
| Open science | 0.001 | 0.002 |
| Research integrity | 0.001 | 0.000 |
| 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".