Bibliometrics research status of critical care medicine
Bibliographic record
Abstract
Objective The bibliomrtrics was used to show the status and hot spots of domestic and foreign literature, original research and the second data analysis about critical care medicine.providing a reference for further research. Methods We systematically searched the papers about critical care medicine from Pubmed, Web of science, Wanfang, VIP, CNKI and CBM database from Jan 1, 1970 to Sep 1, 2017. Excel 2013 was used to extract the basic information. Ucinet 6.0 was used to analyze the collaborations among countries, and HistCite 12.03 was used to conduct time series analysis. Results A total of 353 882 articles were involved, including 1963 meta-analyses. Literatures showed an upward trend year by year, while the rising trend of domestic literature was mainly concentrated from 2005 to 2017. The articles mainly attribute to Europe and the United States. The country with maximum number of publications is USA, whereas the University of Toronto rank first according to the article amount roll. The domestic meta analysis has ranked fourth in the world, but the number of original researches is few and the institutions are scattered. Studies from abroad mainly focus on mechanical ventilation and sepsis, while they focus on pancreatitis in China. Conclusion In the past 50 years, studies about critical care medicine have been increasing year by year which are the hot spot in current medical research. European and American countries are in a dominant position. The level of research in critical care medicine in China is far from that in foreign countries, including the number of documents, the performance of original research, and the lack of cooperation with other countries. Key words: Crirical care; Bibliometrics; Visualization
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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 | 0.018 | 0.111 |
| Meta-epidemiology (narrow) | 0.002 | 0.001 |
| Meta-epidemiology (broad) | 0.006 | 0.005 |
| Bibliometrics | 0.235 | 0.292 |
| Science and technology studies | 0.002 | 0.002 |
| Scholarly communication | 0.008 | 0.005 |
| Open science | 0.002 | 0.003 |
| Research integrity | 0.001 | 0.001 |
| Insufficient payload (model declined to judge) | 0.018 | 0.003 |
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".