[Analysis of research hotspot and frontier of severe coronavirus disease 2019: visual analysis based on CiteSpace].
Bibliographic record
Abstract
OBJECTIVE: To analyze the research hotspot and frontier of severe coronavirus disease 2019 (COVID-19) in China and abroad. METHODS: The CiteSpace software was used to visually analyze the relevant research of severe COVID-19 published by CNKI and Web of Science databases from January 30th to April 20th in 2020. The analysis content included the author of the literature, the publishing institutions, and high-frequency keywords. RESULTS: There were 389 Chinese literatures and 59 English literatures included. Analysis using CiteSpace software showed that there were four large teams in China currently concerning about the research on severe COVID-19. The co-authoring of each team was relatively close, but the teams were lack of cooperation. The main issuing institutions were affiliated hospitals of colleges and universities, but colleges and enterprises had less participation. The authors of English-language publications mainly had five research teams, some of whom had co-authored relationships. The country with the most enormous volume of English-language publications was China, followed by the United States and Canada. The Chinese keyword co-occurrence, clustering and highlighted words analysis showed that the main research areas of severe COVID-19 included clinical features, traditional Chinese medicine treatment, medical imaging, integrated traditional Chinese and Western medicine treatment and so on; nucleic acid detection, clinical features and diagnosis, plague theory and etiology mechanism, traditional Chinese medicine and integrated Chinese and Western medicine treatment, severe COVID-19 combined with diabetes and prognosis research will become future research trends; keyword cluster analysis showed that severe COVID-19, combined chronic underlying diseases, CT imaging characteristics will also become new trends in the field of research. Co-occurrence analysis of keywords in English literatures showed that the main research areas of severe COVID-19 included the names of novel coronavirus, pandemic diseases, infectious diseases, medical supplies distribution, and indicators related to myocardial damage. CONCLUSIONS: Researchers in China and abroad have different concerns about severe COVID-19. Domestic research focuses on the diagnosis and treatment of severe cases, while foreign countries attach importance to epidemic response and prevention.
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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 | 0.026 |
| Meta-epidemiology (narrow) | 0.001 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.003 |
| Bibliometrics | 0.088 | 0.076 |
| 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) | 0.014 | 0.002 |
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".