Background:
Numerous studies have confirmed that helicopter emergency medical services (HEMS) play a positive role in prehospital care. However, few studies have used rigorous bibliometric tools to analyze the knowledge structure and distribution of HEMS research.
Objectives:
The purpose of this study was to use bibliometric methods to conduct a quantitative and qualitative analysis of the HEMS-related literature and to determine the research status and hotspots of HEMS research.
Methods:
CiteSpace was used for bibliometric analysis of the HEMS-related literature retrieved from the Web of Science database from 1989 to 2021.
Results:
A total of 1378 HEMS-related literature were included. Collaboration among countries, authors, and institutions needs to be strengthened. The topics in HEMS research have mainly focused on the effectiveness of helicopter emergency medical services for trauma patients and the comparison of transport effectiveness between helicopters and ground emergency medical services on trauma patient transport. Research over the past 10 years has mainly focused on the application of HEMS in patients with trauma, myocardial infarction, cerebral apoplexy, application of tracheal intubation technology in HEMS, and advanced airway management. In recent years, HEMS research trends have mainly included out-of-hospital cardiac arrest, and transport.
Conclusions:
CiteSpace was used to visualize and analyze the HEMS-related literature, which visually reflected the research status and hot spots, providing references for the topic selection and development direction of HEMS research.
Keywords: CiteSpace, helicopter emergency medical service, visualized analysis
1. Introduction
A helicopter emergency medical service (HEMS) refers to transport activities for saving the lives of patients using general aviation equipment such as helicopters and equipped with professional medical rescue personnel and equipment.[1–3] It is well established that the treatment of patients with severe trauma or cardiovascular disease is highly dependent on time, and studies have shown that transporting patients by helicopter can significantly reduce rescue time and improve patient outcomes.[4–7] HEMS plays an irreplaceable and important role in medical service.[8,9] The number of related studies is increasing rapidly. However, with the increasing demand for comprehensive evidence, there is a need to sort and summarize published studies.
CiteSpace is a popular literature analysis software in the field of bibliometrics, which can analyze the research literature in a certain field at a particular time and then generate scientific knowledge mapping.[10] Through a combination of qualitative and quantitative analyses, CiteSpace can sort out a certain research field, clarify its research structure, identify research hot spots and frontiers, and predict the evolution trend of this field.[11,12] So far, CiteSpace has been used in many research fields. Yang et al used CiteSpace to conduct bibliometric analysis to predict infectious diseases and objectively analyzed the research status and hotspots of infectious disease prediction research in the past 30 years.[13] Chen et al and Zhai et al separately conducted knowledge mapping to reveal research fronts and development trends in COVID-19.[14,15]
To date, no scientific knowledge mapping analysis of HEMS has been conducted. In this study, CiteSpace was used to conduct a knowledge mapping analysis of the HEMS-related literature published in the Web of Science database. The HEMS research status and hotspots are summarized, which may provide references and ideas for subsequent studies.
2. Materials and methods
2.1. Data sources
We used the Web of Science Core Data Set as the source of the literature database, and the advanced retrieval method included the following topics: (air ambulance) OR topic: (helicopter emergency medical service) OR topic: (helicopter emergency medical) OR topic: (air medical rescue); index: (SCI-EXPANDED, SSCI, CPCI-S, CPCI-SSH, BKCI-S, BKCI-SSH); document type: “Article”; and time span: 1898–2021 (retrieved date: January 22, 2021). No language restrictions were applied. Finally, 1378 relevant studies were retrieved, and the search results were saved in download txt format, set as “full record and including the cited references” for output.
2.2. Inclusion and exclusion criteria
We included studies on HEMS and excluded duplicate studies, conference abstracts, and news reports.
2.3. Analysis tool
CiteSpace, developed by Dr Chen Chaomei, School of Information Science and Technology, Drexel University, USA, is an internationally leading visualization application software for multivariate, time-sharing, and dynamic complex network analysis.[1,16] In this study, CiteSpace based on the Java platform was used to draw the map of scientific knowledge. The authors, countries, and research institutions were considered as nodes to analyze the cooperation network. The references were used as nodes to draw a co-citation map and cluster analysis. The keywords were used as nodes for co-occurrence cluster and burst analyses. In knowledge map,[11,16,17] the elements corresponding to different nodes, such as country, institution, author, cited literature, and keyword. The size of the node indicates the number or frequency of publications (i.e., citation count); the larger the node, the higher the number or frequency is (i.e., the number of citations). The various colors in the nodes represent different times. The connecting lines between the nodes reflect the relationship between cooperation and co-citations. The color of the lines indicates the time when cooperation or co-citation first appeared. Centrality numerically measures the communication function of a node in the entire field. The closer the value is to 1, the stronger the correlation between the node and the entire field, indicating that the node has a higher influence on the entire field. Centrality reflects the influence of 1 node on the other nodes.
3. Results
3.1. Analysis of core strengths
In CiteSpace, the node type was set to “Country and Institution,” and the threshold was set to the top 30 per slice, which generated a knowledge map of the core strengths (Fig. 1). The knowledge map contains 82 nodes and 252 links. Since the centrality of all countries and institutions was zero, we analyzed countries and institutions based on the number of published studies. The results showed that the country with the highest number of publications was the United States (383), followed by Germany (188), England (139), Norway (87), and the Netherlands (76) (Table 1). The institution with the highest number of publications was Norwegian Air Ambulance Fdn (44 studies), followed by University Stavanger (33 studies), Oslo University Hosp (26 studies), University Pittsburgh (25 studies), and University Bergen (21 studies), with most of the top 5 institutions from Norway (Table 2).
Figure 1.
Knowledge map of countries and institutions.
Table 1.
Top 10 countries according to the number of publications.
| Ranking | Country | Counts |
|---|---|---|
| 1 | USA | 383 |
| 2 | Germany | 188 |
| 3 | England | 139 |
| 4 | Norway | 87 |
| 5 | The Netherlands | 76 |
| 6 | Australia | 70 |
| 7 | Switzerland | 55 |
| 8 | Austria | 47 |
| 9 | Japan | 43 |
| 10 | Canada | 42 |
Table 2.
Top 10 institutions according to the number of publications.
| Ranking | Institution | Counts |
|---|---|---|
| 1 | Norwegian Air Ambulance Fdn | 44 |
| 2 | Univ Stavanger | 33 |
| 3 | Oslo Univ Hosp | 26 |
| 4 | Univ Pittsburgh | 25 |
| 5 | Univ Bergen | 21 |
| 6 | Radboud Univ Nijmegen | 17 |
| 7 | Univ Surrey | 16 |
| 8 | Med Univ Warsaw | 16 |
| 9 | Univ Maryland | 16 |
| 10 | Haukeland Hosp | 15 |
3.2. Analysis of authors and co-cited authors
In CiteSpace, the node type was set to “Author,” the threshold was set to top 30 per slice, which generated a knowledge map of author collaborations (Fig. 2), the knowledge map contains 329 nodes and 1031 links, the centrality of all authors was zero. Authors with the highest number of publications were GALAZKOWSKI R (20 studies), followed by HELM M (16 studies), LAMPL L (14 studies), GUYETTE FX (13 studies), and STEPHEN J M SOLLID (12 studies) (Table 3). To further analyze the co-citation of authors, the node type was reset to “Cited Author.” The centrality of all co-cited authors was zero. Authors with the highest co-citation count was GALVAGNO SM (137 citations), followed by BROWN JB (136 citations), THOMAS SH (110 citations), BAXT WG (107 citations), and BLEDSOE BE (98 citations) (Table 4).
Figure 2.
Knowledge map of author collaboration.
Table 3.
Top 10 authors according to the number of publications.
| Ranking | Authors | Counts |
|---|---|---|
| 1 | GALAZKOWSKI R | 20 |
| 2 | HELM M | 16 |
| 3 | LAMPL L | 14 |
| 4 | GUYETTE FX | 13 |
| 5 | STEPHEN J M SOLLID | 12 |
| 6 | GUYETTE F | 12 |
| 7 | ROGNAS L | 11 |
| 8 | BROWN J | 10 |
| 9 | BURNS B | 9 |
| 10 | PEITZMAN A | 9 |
Table 4.
Top 10 authors according to co-citation count.
| Ranking | Authors | Counts |
|---|---|---|
| 1 | GALVAGNO SM | 137 |
| 2 | BROWN JB | 136 |
| 3 | THOMAS SH | 110 |
| 4 | BAXT WG | 107 |
| 5 | BLEDSOE BE | 98 |
| 6 | TAYLOR CB | 79 |
| 7 | DAVIS DP | 72 |
| 8 | BAKER SP | 57 |
| 9 | ANDRUSZKOW H | 56 |
| 10 | NEWGARD CD | 52 |
3.3. Analysis of co-cited journals
In CiteSpace, the node type was set to “Cited Journal,” and the threshold was set to the top 50 per slice. The results show that the journal with the highest centrality was JAMA-J AM MED ASSOC, followed by ANN EMERG MED, RESUSCITATION, and so on. Table 5 presents the results.
Table 5.
Top 10 journal co-citations.
| Ranking | Centrality | Co-citation counts | Journal |
|---|---|---|---|
| 1 | 0.66 | 4346 | JAMA-J AM MED ASSOC |
| 2 | 0.52 | 4505 | ANN EMERG MED |
| 3 | 0.51 | 2648 | RESUSCITATION |
| 4 | 0.41 | 2515 | CIRCULATION |
| 5 | 0.18 | 1313 | MED CARE |
| 6 | 0.14 | 1753 | J TRAUMA |
| 7 | 0.13 | 1185 | AM J PUBLIC HEALTH |
| 8 | 0.12 | 3136 | PREHOSP EMERG CARE |
| 9 | 0.12 | 840 | BMJ OPEN |
| 10 | 0.12 | 319 | ACTA ANAESTH SCAND |
3.4. Analysis of the intellectual base
In CiteSpace, the node type was set to “Cited Reference,” the threshold was set to top 30 per slice, the time slice was set to 1 year, and Pathfinder was selected for the pruning, which generated a knowledge map of co-citation reference (Fig. 3). The knowledge map contained 279 nodes and 551 links. Table 6 lists the top 5 co-cited references according to their centrality. Table 7 lists the top 5 co-cited references according to the number of citations. We also conducted a cluster analysis of co-citation references. The details of the co-citation clustering are shown in Figure 3. The results showed that the modularity Q value of clustering was 0.7517, and the mean silhouette value was 0.9003. Modularity Q reflects the relationships and connections among clusters. Generally, modularity Q values between 0.4 and 0.8 are acceptable, and a mean silhouette value close to 1 indicates that references within a cluster contain highly consistent or similar content.[18] In total, 16 clusters were identified, including 5 major clusters (Table 8).
Figure 3.
Knowledge map of co-citation references and co-citation clusters.
Table 6.
Top 5 co-cited references according to centrality.
| Ranking | Centrality | Cited reference | Journal | Representative author | Publication year |
|---|---|---|---|---|---|
| 1 | 0.34 | Heart disease and stroke statistics–2014 update: a report from the American Heart Association | CIRCULATION | Go AS | 2014 |
| 2 | 0.29 | Regional variation in out-of-hospital cardiac arrest incidence and outcome | JAMA-J AM MED ASSOC | Graham Nichol | 2008 |
| 3 | 0.26 | Association of national initiatives to improve cardiac arrest management with rates of bystander intervention and patient survival after out-of-hospital cardiac arrest | JAMA-J AM MED ASSOC | Mads Wissenberg | 2013 |
| 4 | 0.21 | Guidelines for the early management of patients with acute ischemic stroke: a guideline for healthcare professionals from the American Heart Association/American Stroke Association | STROKE | Edward C Jauch | 2013 |
| 5 | 0.18 | Association between helicopter vs ground emergency medical services and survival for adults with major trauma | JAMA-J AM MED ASSOC | Samuel M Galvagno Jr | 2012 |
Table 7.
Top 5 co-cited references according to the number of co-citation counts.
| Ranking | Counts | Cited reference | Journal | Representative author | Publication year |
|---|---|---|---|---|---|
| 1 | 201 | Regional variation in out-of-hospital cardiac arrest incidence and outcome | JAMA-J AM MED ASSOC | Nichol G | 2008 |
| 2 | 167 | Predictors of survival from out-of-hospital cardiac arrest: a systematic review and meta-analysis | CIRC-CARDIOVASC QUAL | Sasson C | 2010 |
| 3 | 167 | Heart disease and stroke statistics–2014 update: a report from the American Heart Association | CIRCULATION | Go AS | 2014 |
| 4 | 133 | Association of national initiatives to improve cardiac arrest management with rates of bystander intervention and patient survival after out-of-hospital cardiac arrest | JAMA-J AM MED ASSOC | Wissenberg M | 2013 |
| 5 | 128 | Global incidences of out-of-hospital cardiac arrest and survival rates: Systematic review of 67 prospective studies | RESUSCITATION | Berdowski J | 2010 |
Table 8.
Five major reference co-citations clusters.
| ID | Size | Mean silhouette | Mean year | Label (LSI) | Label (LLR) | Label (MI) |
|---|---|---|---|---|---|---|
| 0 | 45 | 0.776 | 2013 | Emergency; medical services; resuscitation | Out-of-hospital cardiac arrest (49.15, 1.0E-4); cardiopulmonary resuscitation (24.22, 1.0E-4); | Census tracts (1.21); perfusion index (1.21) |
| 1 | 33 | 0.955 | 2006 | Emergency; medical services | Heart arrest (25.96, 1.0E-4); ventricular fibrillation (25.22, 1.0E-4) | Death sudden (0.52); adult (0.52) |
| 2 | 22 | 0.876 | 2008 | Myocardial infarction; electrocardiography | myocardial infarction (78.7, 1.0E-4); electrocardiography (26.1, 1.0E-4) | Myocardial reperfusion (0.27); ST elevation myocardial infarction (0.27) |
| 3 | 21 | 0.895 | 2009 | Medical services; helicopter emergency | Trauma (31.29, 1.0E-4); helicopter emergency medical services (20.74, 1.0E-4) | Hydrocephalus (0.54); helicopter retrieval (0.54) |
| 4 | 19 | 0.968 | 2011 | Emergency; medical services | Stroke (68.9, 1.0E-4); thrombolysis (27.8, 1.0E-4) | Stents (0.56); Chicago (0.56) |
LLR = locally linear regression algorithm, LSI = log-likelihood ratio, MI = mutual information.
3.5. Analysis of hotspots
In CiteSpace, the node type was set to “Keyword,” the threshold was set to top 30, time span: 2011–2021, Pathfinder was selected for the pruning, which generated a map of keyword co-occurrence (Fig. 4), the knowledge map contains 112 nodes and 408 links. We also performed a cluster analysis of the keywords. Figure 4 presents the details of the keyword clusters. Seven keyword clusters were identified, including 3 major keyword clusters (Table 9). The modularity Q value was 0.4218 and the mean silhouette value was 0.7686. Keywords with the highest centrality were trauma (0.21), followed by helicopter (0.18), management (0.14), trauma patient (0.14), air ambulance (0.12), service (0.12), resuscitation (0.10), air (0.10), impact (0.09), and guideline (0.09) (Table 10).
Figure 4.
Knowledge map of keywords co-occurrence and clusters.
Table 9.
Three major keyword clusters.
| ID | Size | Mean silhouette | Mean year | Label (LSI) | Label (LLR) | Label (MI) |
|---|---|---|---|---|---|---|
| 0 | 20 | 0.806 | 2013 | Emergency; of-hospital cardiac arrest | Out-of-hospital cardiac arrest (125.18, 1.0E-4); cardiopulmonary resuscitation (103.4, 1.0E-4) | Oad-distributing band CPR device (0.8); mhealth (0.8) |
| 1 | 18 | 0.611 | 2013 | Emergency; medical services | Impact (27.85, 1.0E-4); service (24.71, 1.0E-4) | Hemodynamic (0.63); component (0.63) |
| 2 | 17 | 0.839 | 2011 | Emergency; medical services | Stroke (50.48, 1.0E-4); mortality (24.84, 1.0E-4) | Critical issue (0.84); emergency service hospital (0.84) |
LLR = locally linear regression algorithm, LSI = log-likelihood ratio, MI = mutual information.
Table 10.
High-frequency keywords.
| Ranking | Keywords | Centrality |
|---|---|---|
| 1 | Trauma | 0.21 |
| 2 | Helicopter | 0.18 |
| 3 | Management | 0.14 |
| 4 | Trauma patient | 0.14 |
| 5 | Air ambulance | 0.12 |
| 6 | Service | 0.12 |
| 7 | Resuscitation | 0.10 |
| 8 | Air | 0.10 |
| 9 | Impact | 0.09 |
| 10 | Guideline | 0.09 |
Burst terms appeared in a short period or a sudden increase in the frequency of use. It can detect a sudden increase in research interest in a certain subject area, and then identify and track the trends of the research frontier.[19,20] In this study, CiteSpace was used to conduct the burst analysis of keywords. In total, 20 burst keywords were identified and sorted by the beginning year of the burst (Fig. 5).
Figure 5.
Knowledge map of keywords co-occurrence and clusters.
4. Discussion
4.1. Core strengths
In this study, the United States published more HEMS-related literature than any other country, indicating that it had a very high global influence on HEMS research, followed by Germany, England, and other countries. The results showed that the top 5 countries were all economically developed, indicating that the comprehensive strength of 1 country may influence the development of HEMS research. Among the top 5 institutions according to the number of publications, most institutions were in Norway. The institutions in Norway occupied the top rankings in terms of absolute contribution and influence, which was inconsistent with the analysis of the impact of countries in the field of HEMS research. This shows that there were some significant differences between countries and institutions in the field of HEMS research, which may provide valuable information for researchers to choose suitable countries or institutions for collaboration. However, the centrality of all countries and institutions was zero, suggesting that cooperation among countries and institutions in the field of HEMS research still needs to be further strengthened.
4.2. Core authors
A group of authors with a certain academic influence is one of the epitomes of scientific research activities in a certain discipline. It is helpful to understand the new trends and development trends of a certain research field by studying authors with high influence and evaluating authors with active publications. The academic influence of an author can be measured by the number of articles published and the frequency of citations, which reflect the research productivity and academic influence of an author from the perspective of quantity and quality, respectively.[21] In this study, GALAZKOWSKI R[22] published the most number of publications, which involved a wide range of research topics on out-of-hospital cardiac arrest, transport security, mechanical ventilation, pretransport intervention, mortality rates, and hospitalizations. The top 7 authors, all with >10 publications, were high-profile authors in HEMS research. GALVAGNO SM ranked first in citation frequency, and his research topic mainly focused on the treatment of trauma. However, the centrality of all collaborators and author co-citations was zero, suggesting that a core group of influential authors has not been formed in this field. These results indicate that cooperation among authors in HEMS research still needs to be further strengthened.
4.3. Core journals
Through the analysis of journal co-citations, we preliminarily identified the core journals in the field of HEMS research, which reflected the quality and impact of articles published in these journals. Journals with high citation frequency included JAMA-J AM MED ASSOC, ANN EMERG MED, PREHOSP EMERG CARE, and RESUSCITATION. Journals with a higher centrality included JAMA-J AM MED ASSOC, ANN EMERG MED, RESUSCITATION, and CIRCULATION. In general, the centrality of the top 10 journals was >0.1, indicating that the articles published in these journals were of high quality and had a greater impact on HEMS research. Combining citation frequency analysis and centrality analysis, we found that JAMA-J AM MED ASSOC and ANN EMERG MED were both highly cited and high-centrality journals. JAMA-J AM MED ASSOC is a comprehensive clinical medical journal sponsored by the American Medical Association, and it is one of the 4 internationally recognized medical journals.[23] ANN EMERG MED is an official journal of the American College of Emergency Physicians, an international peer-reviewed journal dedicated to improving the quality of care by publishing the highest quality science in emergency medicine and related medical specialties.[24] These 2 journals published many high-quality articles, and their academic influence was higher than that of other similar journals. These are important sources of literature and play an important role in the field of HEMS research.
4.4. Intellectual base
In bibliometrics, references in cutting-edge articles in a certain research field can be analyzed to understand the intellectual base of the subject.[11,12] In this study, by analyzing the cited frequency of references, we found that the subjects of references with high cited frequency mainly focused on the effectiveness of HEMS for trauma patients, the comparison of transport effectiveness between helicopter and ground emergency medical services on trauma patient transport.[25–29]
In addition, we conducted a cluster analysis of co-citation references to further analyze the intellectual base of HEMS. The clusters shown in Figure 3 are relatively concentrated, nondispersed, and overlapping, indicating that the intellectual base of HEMS is relatively concentrated.
4.5. Hotspots
Keywords are condensed research topics, which can be used to reveal the internal relationship of knowledge in a certain subject area, and research hotspots can be revealed through the analysis of high-frequency keywords.[30] Therefore, we analyzed high-frequency keywords from the last 10 years to identify the hotspots of HEMS research. Through statistical analysis of high-frequency keywords in HEMS research, we found that the highest frequency keyword was trauma, indicating that trauma was one of the hotspots in the field of HEMS research. Other high-frequency keywords, such as helicopter, management, trauma patient, air ambulance, service, resuscitation, air, impact, and guidelines, revealed other research hotspots in this field.
In addition, the results of keyword clustering showed a total of 7 clusters, including 3 major clusters. The topics included in these 3 clusters mainly focused on the application of HEMS to patients with trauma, myocardial infarction, cerebral apoplexy, application of tracheal intubation technology in HEMS, and advanced airway management.
Keyword burst analysis reflected the research frontier and trends in the field of HEMS research. It is not difficult to infer from the results of keyword burst analysis that acute myocardial infarction, ventricular fibrillation, and cardiac arrest are still the focus of HEMS research in recent years, which on the other hand shows that HEMS has certain advantages in the treatment of these patients. Moreover, the keywords with strong burst intensity and lasting until 2021 included out-of-hospital cardiac arrest, and transport. The above results further revealing the latest hot spots and trends in the field of HEMS research, providing references for related researchers, and promoting further development of HEMS research.
4.6. Limitation
The limitation of this study was that, using CiteSpace to generate the visual atlas, there was no standardized setting process for time partition, threshold value, and cutting mode for the moment. The setting method closest to the real result can only be explored through repeated attempts, leading to a certain degree of bias in the obtained results. Moreover, Web of Science Core Data Set was chosen as the only data source in this study, and there may be limitations in the research results.
5. Conclusion
In conclusion, CiteSpace was used in this study to conduct a visual analysis of HEMS-related literature in the Web of Science database from 1989 to 2021, including countries, institutions, authors, journals, literature, and keywords. The results show that collaboration among countries, authors, and institutions needs to be strengthened. Most of the journals that published HEMS articles were of high quality. The topics in the HEMS study mainly focused on the effectiveness of HEMS for trauma patients and the comparison of transport effectiveness between helicopter and ground emergency medical services for trauma patient transport. The intellectual base of HEMS is relatively concentrated. Research hotspots in the past 10 years have mainly focused on the application of HEMS in patients with trauma, myocardial infarction, cerebral apoplexy, application of tracheal intubation technology in HEMS, and advanced airway management. In recent years, the research trends in HEMS have mainly included out-of-hospital cardiac arrest and transport. This study conducted a comprehensive knowledge map analysis of HEMS, which may provide a reference for further research in this field.
Author contributions
Conceptualization: Cheng Peng, Pan Su.
Data curation: Cheng Peng
Formal analysis: Cheng Peng, Pan Su.
Investigation: Cheng Peng
Methodology: Cheng Peng, Pan Su.
Software: Cheng Peng, Pan Su.
Visualization: Cheng Peng, Pan Su.
Writing–original draft: Cheng Peng, Pan Su.
Writing–review & editing: Pan Su
Abbreviations:
- BKCI-S =
- book citation index – science
- BKCI-SSH =
- book citation index – social science & humanities
- CPCI-S =
- conference proceedings citation index – science
- CPCI-SSH =
- conference proceedings citation index – social science & humanities
- HEMS =
- helicopter emergency medical service
- SCI-EXPANDED =
- science citation index expanded
- SSCI =
- social sciences citation index
All data generated or analyzed during this study are included in this published article [and its supplementary information files].
How to cite this article: Peng C, Su P. Visualized analysis of research on helicopter emergency medical service. Medicine 2022;101:36(e30463).
This work did not require ethical approval because it did not involve any human or animal research.
The authors have no funding and conflicts of interest to disclose.
References
- [1].Albrecht R, Knapp J, Theiler L, et al. Transport of COVID-19 and other highly contagious patients by helicopter and fixed-wing air ambulance: a narrative review and experience of the Swiss air rescue Rega. Scand J Trauma Resusc Emerg Med. 2020;28:40. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [2].Sajjadi SM, Rahmanian Koushkaki F, Mousavi-Roknabadi RS, et al. Transferred patients by Fars Province’s Helicopter Emergency Medical Service (HEMS); a 2-years cross-sectional study in Southern Iran. Bull Emerg Trauma. 2021;9:21–7. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [3].Griggs JE, Barrett JW, Ter Avest E, et al. Helicopter emergency medical service dispatch in older trauma: time to reconsider the trigger? Scand J Trauma Resusc Emerg Med. 2021;29:62. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [4].Risgaard B, Draegert C, Baekgaard JS, et al. Impact of physician-staffed helicopters on pre-hospital patient outcomes: a systematic review. Acta Anaesthesiol Scand. 2020;64:691–704. [DOI] [PubMed] [Google Scholar]
- [5].Thomas SH, Thomas SW, Thomas SA, et al. Helicopter emergency medical services literature 1972-2017: characteristics and trends. Air Med J. 2019;38:115–24. [DOI] [PubMed] [Google Scholar]
- [6].Bläsius FM, Horst K, Brokmann JC, et al. Helicopter emergency medical service and hospital treatment levels affect survival in pediatric trauma patients. J Clin Med. 2021;10:837. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [7].Andruszkow H, Frink M, Zeckey C, et al. Merits and capabilities of helicopter emergency medical service (HEMS) in traumatized patients. Technol Health Care. 2012;20:435–44. [DOI] [PubMed] [Google Scholar]
- [8].Kim HW, Yun JH. Treatment experiences of traumatic brain injury patients using doctor-helicopter emergency medical service: early data in a regional trauma center. Korean J Neurotrauma. 2020;16:157–65. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [9].Świeżewski SP, Rzońca P, Panczyk M, et al. Polish helicopter emergency medical service (HEMS) response to stroke: a five-year retrospective study. Med Sci Monit. 2019;25:6547–53. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [10].Paul S, Kokossis A, Gage H, et al. A semantically enabled formalism for the knowledge management of Parkinson’s disease. Med Inform Internet Med. 2006;31:101–20. [DOI] [PubMed] [Google Scholar]
- [11].Chen CM. CiteSpace II: detecting and visualizing emerging trends and transient patterns in scientific literature. J Am Soc Inf Sci Tec. 2006;57:359–77. [Google Scholar]
- [12].Chen C, Ibekwe-SanJuan F, Hou JH. The structure and dynamics of cocitation clusters: a multiple perspective cocitation analysis. J Am Soc Inf Sci Tec. 2010;61:1386–409. [Google Scholar]
- [13].Yang W, Zhang J, Ma R. The prediction of infectious diseases: a bibliometric analysis. Int J Environ Res Public Health. 2020;17:6218. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [14].Chen Y, Guo YB, Guo R, et al. Visual analysis of coronavirus disease 2019 (COVID-19) studies based on bibliometrics. Zhongguo Zhong Yao Za Zhi. 2020;45:2239–48. [DOI] [PubMed] [Google Scholar]
- [15].Zhai F, Zhai Y, Cong C, et al. Research progress of coronavirus based on bibliometric analysis. Int J Environ Res Public Health. 2020;17:3766. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [16].Synnestvedt MB, Chen C, Holmes JH. CiteSpace II: visualization and knowledge discovery in bibliographic databases. AMIA Annu Symp Proc. 2005;2005:724–8. [PMC free article] [PubMed] [Google Scholar]
- [17].Zhao DQ. Probe into several problems relating to mapping knowledge domains based on CiteSpace. Inf Stud. 2012;35:56–8. [Google Scholar]
- [18].Hu JH, Meng QG. Knowledge map analysis of Chinese medicine treatment of type 2 diabetes based on CiteSpace. Chin J Tradit Chin Med. 2017;32:4102–6. [Google Scholar]
- [19].Chen Y, Chen CM, Liu ZY, et al. Methodological functions of CiteSpace knowledge graph. Stud Sci. 2015;33:242–53. [Google Scholar]
- [20].Pu DQ, Liu Z, Zhou C, et al. Research status and hot trends in atherosclerosis genomics between 2010 and 2019. Acta Acad Med Sin. 2021;43:92–100. [DOI] [PubMed] [Google Scholar]
- [21].Chen C, Hu Z, Liu S, et al. Emerging trends in regenerative medicine: a scientometric analysis in CiteSpace. Expert Opin Biol Ther. 2012;12:593–608. [DOI] [PubMed] [Google Scholar]
- [22].Rzonca P, Galazkowski R, Panczyk M, et al. Polish helicopter emergency medical service (HEMS) response to out-of-hospital cardiac arrest (OHCA): a retrospective study. Med Sci Monit. 2018;24:6053–8. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [23].JAMA Network. JAMA-J AM MED ASSOC. Available at: https://www.ama-assn.org/ [access date March 7, 2021].
- [24].Annals of Emergency Medicine. Aims & Scope. Available at: https://www.annemergmed.com/content/aims [access date March 7, 2021].
- [25].Galvagno SJ, Haut ER, Zafar SN, et al. Association between helicopter vs ground emergency medical services and survival for adults with major trauma. JAMA. 2012;307:1602–10. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [26].Brown JB, Stassen NA, Bankey PE, et al. Helicopters and the civilian trauma system: national utilization patterns demonstrate improved outcomes after traumatic injury. J Trauma. 2010;69:10301034–1036. [DOI] [PubMed] [Google Scholar]
- [27].Taylor CB, Stevenson M, Jan S, et al. A systematic review of the costs and benefits of helicopter emergency medical services. Injury. 2010;41:10–20. [DOI] [PubMed] [Google Scholar]
- [28].Delgado MK, Staudenmayer KL, Wang NE, et al. Cost-effectiveness of helicopter versus ground emergency medical services for trauma scene transport in the United States. Ann Emerg Med. 2013;62:351–364.e19. [DOI] [PMC free article] [PubMed] [Google Scholar]
- [29].Butler DP, Anwar I, Willett K. Is it the H or the EMS in HEMS that has an impact on trauma patient mortality? A systematic review of the evidence. Emerg Med J. 2010;27:692–701. [DOI] [PubMed] [Google Scholar]
- [30].Fu L, Sun Z, He L, et al. Global long-term care research: a scientometric review. Int J Environ Res Public Health. 2019;16:2077. [DOI] [PMC free article] [PubMed] [Google Scholar]