Emergency and rapid response systems: a bibliometric analysis
Bibliographic record
Abstract
Background: The emergency rapid response system (RRS) can reduce the mortality of hospitalized patients, and its core is the activation criteria and the rapid response team (RRT). This study adopted a bibliometric method to analyze the research status of RRSs for hospitalized patients. Methods: The Science Citation Index Expanded (SCI-E) database was searched using the keywords "emergency" and "rapid response system", and the search results were analyzed using CiteSpace software. The retrieved data included the annual distribution of studies and literature citations; the source country of the literature; the distribution of institutions and authors of the literature; the cooperation between countries, institutions, and authors; the distribution of journals that published the literature, and the use of keywords in the literature. Results: A total of 1,320 research papers were found, with a total of 29,920 citations. The number of papers and their citations increased yearly. The top 5 countries in terms of number of publications were the United States, Australia, China, the United Kingdom, and Canada. The top 5 countries in terms of centrality were the United States, the United Kingdom, Argentina, the Czech Republic, and Switzerland. The research institutions were mainly located in developed countries, such as the United States and Australia. There was relatively little collaboration between researchers. The journals that published the literature mainly specialized in critical care medicine and emergency medicine. The keyword analysis revealed that most studies focused on medical emergency teams (METs) and mortality. Conclusions: There were few studies related to the emergency RRS for hospitalized patients. The majority of studies were from developed countries and mainly focused on the impact of team building and the effect of the RRS on mortality.
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How this classification was reachedexpand
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 | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.028 | 0.046 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.000 |
| Insufficient payload (model declined to judge) | 0.003 | 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, unvalidatedMachine predicted; both teacher heads agree on what is shown here.
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".