Impact factor correlations with Scimago Journal Rank, Source Normalized Impact per Paper, Eigenfactor Score, and the CiteScore in Radiology, Nuclear Medicine & Medical Imaging journals

2019 ◽  
Vol 124 (6) ◽  
pp. 495-504 ◽  
Author(s):  
Moises Villaseñor-Almaraz ◽  
Juan Islas-Serrano ◽  
Chiharu Murata ◽  
Ernesto Roldan-Valadez
Keyword(s):  
Nuclear Medicine ◽  
Medical Imaging ◽  
Impact Factor ◽  
Scimago Journal Rank

scholarly journals Peer review practices by medical imaging journals

2020 ◽  
Vol 11 (1) ◽  
Author(s):  
Thomas C. Kwee ◽  
Hugo J. A. Adams ◽  
Robert M. Kwee
Keyword(s):  
Nuclear Medicine ◽  
Medical Imaging ◽  
Impact Factor ◽  
Peer Review ◽  
Correlation Coefficient ◽  
Manuscript Submission ◽  
Peer Reviews ◽  
Journal Citation Reports ◽  
Review Model ◽  
Double Blinded

Abstract Objective To investigate peer review practices by medical imaging journals. Methods Journals in the category "radiology, nuclear medicine and medical imaging" of the 2018 Journal Citation Reports were included. Results Of 119 included journals, 62 (52.1%) used single-blinded peer review, 49 (41.2%) used double-blinded peer review, two (1.7%) used open peer review and one (0.8%) used both single-blinded and double-blinded peer reviews, while the peer review model of five journals (4.2%) remained unclear. The use of single-blinded peer review was significantly associated with a journal’s impact factor (correlation coefficient of 0.218, P = 0.022). On subgroup analysis, only subspecialty medical imaging journals had a significant association between the use of single-blinded peer review and a journal’s impact factor (correlation coefficient of 0.354, P = 0.025). Forty-eight journals (40.3%) had a reviewer preference option, 48 journals (40.3%) did not have a reviewer recommendation option, and 23 journals (19.3%) obliged authors to indicate reviewers on their manuscript submission systems. Sixty-four journals (53.8%) did not provide an explicit option on their manuscript submission Web site to indicate nonpreferred reviewers, whereas 55 (46.2%) did. There were no significant associations between the option or obligation to indicate preferred or nonpreferred reviewers and a journal’s impact factor. Conclusion Single-blinded peer review and the option or obligation to indicate preferred or nonpreferred reviewers are frequently employed by medical imaging journals. Single-blinded review is (weakly) associated with a higher impact factor, also for subspecialty journals. The option or obligation to indicate preferred or nonpreferred reviewers is evenly distributed among journals, regardless of impact factor.

Radiology During the COVID-19 Pandemic: Mapping Radiology Literature in 2020

2021 ◽  
Vol 18 ◽  
Author(s):  
Nosaiba Al-Ryalat ◽  
Lna Malkawi ◽  
Ala'a Abu Salhiyeh ◽  
Faisal Abualteen ◽  
Ghaida Abdallah ◽  
...  
Keyword(s):  
Machine Learning ◽  
Computed Tomography ◽  
Nuclear Medicine ◽  
Medical Imaging ◽  
Core Collection ◽  
Web Of Science ◽  
Vos Viewer ◽  
Literature Mapping ◽  
Link Strength ◽  
Linkage Strength

Objectives: Our aim was to assess articles published in the field of radiology, nuclear medicine, and medical imaging in 2020, analyzing the linkage of radiology-related topics with coronavirus disease 2019 (COVID-19) through literature mapping, along with a bibliometric analysis for publications. Methods: We performed a search on Web of Science Core Collection database for articles in the field of radiology, nuclear medicine, and medical imaging published in 2020. We analyzed the included articles using VOS viewer software, where we analyzed the co-occurrence of keywords, which represents major topics discussed. Of the resulting topics, literature map created, and linkage analysis done. Results: A total of 24,748 articles were published in the field of radiology, nuclear medicine, and medical imaging in 2020. We found a total of 61,267 keywords, only 78 keywords occurred more than 250 times. COVID-19 had 449 occurrences, 29 links, with a total link strength of 271. MRI was the topic most commonly appearing in 2020 radiology publications, while “computed tomography” has the highest linkage strength with COVID-19, with a linkage strength of 149, representing 54.98% of the total COVID-19 linkage strength, followed by “radiotherapy, and “deep and machine learning”. The top cited paper had a total of 1,687 citations. Nine out of the 10 most cited articles discussed COVID-19 and included “COVID-19” or “coronavirus” in their title, including the top cited paper. Conclusion: While MRI was the topic that dominated, CT had the highest linkage strength with COVID-19 and represent the topic of top cited articles in 2020 radiology publications.

scholarly journals Association between the Rankings of Top Bioinformatics and Medical Informatics Journals and the Scholarly Reputations of Chief Editors

Publications ◽  
2021 ◽  
Vol 9 (3) ◽  
pp. 42
Author(s):  
Salim Sazzed
Keyword(s):  
Impact Factor ◽  
Medical Informatics ◽  
Correlation Coefficients ◽  
Chief Editor ◽  
Prediction Errors ◽  
H Index ◽  
Quantitative Metrics ◽  
High Prediction ◽  
Scimago Journal Rank ◽  
Journal Impact

The scientometric indices, such as the journal Impact Factor (IF) or SCImago Journal Rank (SJR), often play a determining role while choosing a journal for possible publication. The Editor-in-Chief (EiC), also known as a lead editor or chief editor, usually decides the outcomes (e.g., accept, reject) of the submitted manuscripts taking the reviewer’s feedback into account. This study investigates the associations between the EiC’s scholarly reputation (i.e., citation-level metrics) and the rankings of top Bioinformatics and Computational Biology (BCB) and Medical Informatics (MI) journals. I consider three scholarly indices (i.e., citation, h-index, and i-10 index) of the EiC and four scientometric indices (i.e., h5-index, h5-median, impact factor, and SJR) of various journals. To study the correlation between scientometric indices of the EiC and journal, I apply Spearman (ρ) and Kendall (τ) correlation coefficients. Moreover, I employ machine learning (ML) models for the journal’s SJR and IF predictions leveraging the EiC’s scholarly reputation indices. The analysis reveals no correlation between the EiC’s scholarly achievement and the journal’s quantitative metrics. ML models yield high prediction errors for SJR and IF estimations, which suggests that the EiC’s scholarly indices are not good representations of the journal rankings.

scholarly journals O41: JOURNAL RANKING METHODS AND THEIR APPLICATION IN GENERAL SURGERY - A SYSTEMATIC REVIEW

2021 ◽  
Vol 108 (Supplement_1) ◽  
Author(s):  
SR Arshad ◽  
ER Gallivan ◽  
H Skinner ◽  
J Kerry ◽  
JR Burke ◽  
...  
Keyword(s):  
Impact Factor ◽  
Objective Assessment ◽  
Ranking Method ◽  
Systematic Search ◽  
Journal Ranking ◽  
Ranking Methods ◽  
Academic Surgery ◽  
Ovid Medline ◽  
Based Medicine ◽  
Scimago Journal Rank

Abstract Introduction Bibliometrics enable the objective assessment of a journals quality and prestige, making them vital to academic surgical integrity. This paper systematically reviews the evidence for current journal-ranking platforms. Method An initial systematic search identified published journal bibliometrics on 5th November 2019. Studies assessing journal bibliometrics were identified through a second systematic search through OVID Medline database (PROSPERO:159689) on 18th November 2019. Non-English papers and those solely referring to author and institutional ranking methods were excluded. A coding framework was developed to assess strengths and limitations of journal bibliometrics. Result The systematic search returned 2480 articles. All abstracts were reviewed leaving 474 for full assessment. 306 papers were included in the final synthesis. 12 journal bibliometrics were identified. SCImago Journal Rank (SJR) was deemed the most robust journal ranking method. Impact Factor (IF) is the most commonly used but has clear limitations. Altmetrics are likely to become more important. Ranking the Top 25 general surgical journals using SJR and IF results in clear discordance. Conclusion Impact Factor is susceptible to manipulation by journals putting its reliability into question. This has huge implications for academic surgery, the appraisal of evidence and its downstream assessment for inclusion into clinical guidelines and practice. SJR is more robust and it is recommended that this ranking method is adopted by the academic surgical community going forward. Take-home message The widely used Impact Factor can be manipulated by journals, which has implications for academic surgery and evidence-based medicine. SCImago Journal Rank is more robust and should be adopted by the academic surgical community.

scholarly journals E-learning for medical imaging specialists: introducing blended learning in a nuclear medicine specialist course

2017 ◽  
Vol 6 (7) ◽  
pp. 205846011772085 ◽  
Author(s):  
Torjan Haslerud ◽  
Andreas Julius Tulipan ◽  
Robert M Gray ◽  
Martin Biermann
Keyword(s):  
Nuclear Medicine ◽  
Medical Students ◽  
Medical Imaging ◽  
Blended Learning ◽  
Medical Specialists ◽  
Supplementary Online Material ◽  
E Learning ◽  
Course Format ◽  
Professional Guidelines ◽  
Nuclear Medicine Specialist

Background While e-learning has become an important tool in teaching medical students, the training of specialists in medical imaging is still dominated by lecture-based courses. Purpose To assess the potential of e-learning in specialist education in medical imaging. Material and Methods An existing lecture-based five-day course in Clinical Nuclear Medicine (NM) was enhanced by e-learning resources and activities, including practical exercises. An anonymized survey was conducted after participants had completed and passed the multiple choice electronic course examination. Results Twelve out of 15 course participants (80%) responded. Overall satisfaction with the new course format was high, but 25% of the respondents wanted more interactive elements such as discussions and practical exercises. The importance of lecture handouts and supplementary online material such as selected original articles and professional guidelines was affirmed by all the respondents (92% fully, 8% partially), while 75% fully and 25% partially agreed that the lectures had been interesting and relevant. Conclusion E-learning represents a hitherto unrealized potential in the education of medical specialists. It may expedite training of medical specialists while at the same time containing costs.

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