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Chan A, Flash MJ, Guo T, Zattra O, Boms O, Succi MD, Hirsch JA. Trends in Academic Productivity Among Radiologists During the COVID-19 Pandemic. J Am Coll Radiol 2023; 20:276-281. [PMID: 36496090 PMCID: PMC9729584 DOI: 10.1016/j.jacr.2022.10.005] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 06/01/2022] [Revised: 09/21/2022] [Accepted: 10/03/2022] [Indexed: 12/13/2022]
Abstract
PURPOSE There is a scarcity of literature examining changes in radiologist research productivity during the COVID-19 pandemic. The current study aimed to investigate changes in academic productivity as measured by publication volume before and during the COVID-19 pandemic. METHODS This single-center, retrospective cohort study included the publication data of 216 researchers consisting of associate professors, assistant professors, and professors of radiology. Wilcoxon's signed-rank test was used to identify changes in publication volume between the 1-year-long defined prepandemic period (publications between May 1, 2019, and April 30, 2020) and COVID-19 pandemic period (May 1, 2020, to April 30, 2021). RESULTS There was a significantly increased mean annual volume of publications in the pandemic period (5.98, SD = 7.28) compared with the prepandemic period (4.98, SD = 5.53) (z = -2.819, P = .005). Subset analysis demonstrated a similar (17.4%) increase in publication volume for male researchers when comparing the mean annual prepandemic publications (5.10, SD = 5.79) compared with the pandemic period (5.99, SD = 7.60) (z = -2.369, P = .018). No statistically significant changes were found in similar analyses with the female subset. DISCUSSION Significant increases in radiologist publication volume were found during the COVID-19 pandemic compared with the year before. Changes may reflect an overall increase in academic productivity in response to clinical and imaging volume ramp down.
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Affiliation(s)
- Alex Chan
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Faculty of Medicine, McMaster University, Hamilton, Ontario, Canada; and Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Moses J.E. Flash
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; and Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Teddy Guo
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Faculty of Medicine, McMaster University, Hamilton, Ontario, Canada; and Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Ottavia Zattra
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; and Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Okechi Boms
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; and Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts
| | - Marc D. Succi
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Associate Chair, Innovation and Commercialization, Mass General Brigham Enterprise Radiology; and Member, ACR Economics Committee,Corresponding authors and reprints: Marc D. Succi, MD, Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA 02114
| | - Joshua A. Hirsch
- Medically Engineered Solutions in Healthcare Incubator, Innovations in Operations Research Center (MESH IO), Massachusetts General Hospital, Boston, Massachusetts; Harvard Medical School, Boston, Massachusetts; Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Vice Chair Procedural Services, Director Interventional Neuroradiology, Chief Interventional Spine, Associate Department Quality Chair at Massachusetts General Hospital; Councilor to the ACR for Society of NeuroInterventional Surgery; Chair, Future Trends and Academic Committees ACR; Deputy Editor; JNIS; and Senior Affiliate Research Fellow, Neiman Health Policy Institute Joint Grant Program,Joshua A. Hirsch, MD, Massachusetts General Hospital, Department of Radiology, 55 Fruit Street, Boston, MA 02114
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Gregory J, Welliver S, Chong J. Top 10 Reviewer Critiques of Radiology Artificial Intelligence (AI) Articles: Qualitative Thematic Analysis of Reviewer Critiques of Machine Learning/Deep Learning Manuscripts Submitted to JMRI. J Magn Reson Imaging 2020; 52:248-254. [PMID: 31943495 DOI: 10.1002/jmri.27035] [Citation(s) in RCA: 19] [Impact Index Per Article: 4.8] [Reference Citation Analysis] [Abstract] [Key Words] [Journal Information] [Subscribe] [Scholar Register] [Received: 09/21/2019] [Revised: 12/09/2019] [Accepted: 12/11/2019] [Indexed: 12/19/2022] Open
Abstract
BACKGROUND Classical machine learning (ML) and deep learning (DL) articles have rapidly captured the attention of the radiology research community and comprise an increasing proportion of articles submitted to JMRI, of variable reporting and methodological quality. PURPOSE To identify the most frequent reviewer critiques of classical ML and DL articles submitted to JMRI. STUDY TYPE Qualitative thematic analysis. POPULATION In all, 1396 manuscript journal articles submitted to JMRI for consideration in 2018, with thematic analysis performed of reviewer critiques of 38 artificial intelligence (AI) articles, comprised of 24 ML and 14 DL articles, from January 9, 2018 to June 2, 2018. FIELD STRENGTH/SEQUENCE N/A. ASSESSMENT After identifying and sampling ML and DL articles, and collecting all reviews, qualitative thematic analysis was performed to identify major and minor themes of reviewer critiques. STATISTICAL TESTS Descriptive statistics provided of article characteristics, and thematic review of major and minor themes. RESULTS Thirty-eight articles were sampled for thematic review: 24 (63.2%) focused on classical ML and 14 (36.8%) on DL. The overall acceptance rate of classical ML/DL articles was 28.9%, similar to the overall 2017-2019 acceptance rate of 23.1-28.1%. These articles resulted in 72 reviews analyzed, yielding a total 713 critiques that underwent formal thematic analysis consensus encoding. Ten major themes of critiques were identified, with 1-Lack of Information as the most frequent, comprising 268 (37.6%) of all critiques. Frequent minor themes of critiques concerning ML/DL-specific recommendations included performing basic clinical statistics such as to ensure similarity of training and test groups (N = 26), emphasizing strong clinical Gold Standards for the basis of training labels (N = 19), and ensuring strong radiological relevance of the topic and task performed (N = 16). DATA CONCLUSION Standardized reporting of ML and DL methods could help address nearly one-third of all reviewer critiques made. LEVEL OF EVIDENCE 4 Technical Efficacy Stage: 1 J. Magn. Reson. Imaging 2020;52:248-254.
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Affiliation(s)
- Jules Gregory
- Department of Radiology, Beaujon Hospital, Paris Nord Val de Seine Hospitals, APHP, Clichy, France and Université de Paris, INSERM U1153, Epidemiology and Biostatistics Research Center (CRESS), Methods of Therapeutic Evaluation of Chronic Diseases (METHODS) Team, Paris, France
| | - Sara Welliver
- JMRI Editorial Office, J&J Editorial, 201 Shannon Oaks Circle, Cary, North Carolina, 27511, USA
| | - Jaron Chong
- Department of Radiology, McGill University, Montreal, Québec, Canada
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Pinson JA. Who? What? Where? A snapshot of Nuclear Medicine Research Presentations from recent ANZSNM conferences in Australia and New Zealand. ASIA OCEANIA JOURNAL OF NUCLEAR MEDICINE & BIOLOGY 2020; 8:123-131. [PMID: 32715000 PMCID: PMC7354245 DOI: 10.22038/aojnmb.2020.44525.1300] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Subscribe] [Scholar Register] [Received: 11/22/2019] [Revised: 02/13/2020] [Accepted: 02/28/2020] [Indexed: 11/06/2022]
Abstract
OBJECTIVES The aims of this study were to: 1) discover location (by city) of contributors to poster and oral presentations at recent ANZSNM conferences; 2) determine the nuclear medicine themes most commonly explored; 3) establish institutions producing the highest number of oral and poster abstracts and 4) determine publication rates of conference abstracts to full papers from recent ANZSNM conferences. METHODS Retrospective analysis of abstracts published in the Internal Medicine Journal Special Issues 2014-2019 identified 614 abstracts. Invited plenary speaker abstracts were excluded. Descriptive statistics were used in data analysis. Conference abstracts were analysed using the following criteria: poster or oral presentation, author/s, city location, hospital and subject matter. Themes defined by the ANZSNM conference committee for abstract submission were: cardiology, oncology, neurology, therapy, renal/urology, gastrointestinal, paediatrics, musculoskeletal, infection/inflammation, technology, physics, radiation safety, radiopharmacy/radiochemistry, education, or general. Retrospective analysis of 555 conference abstracts (excluding New Zealand and International, 59 abstracts) using Google Scholar, Pubmed and Google databases was undertaken. Abstract titles, key words, institutions and/or authors' names were used to find peer-reviewed papers. Identified papers were authenticated through either open access, publicly available author information or Monash University's library access. Published paper citations were also recorded (up to 1st July 2019). RESULTS Analysis of 614 abstracts 2014 - 2019 was performed. Over five years, the average number of poster abstracts was 67.8 and oral 55.0. Sydney submitted the highest number of poster abstracts, while Melbourne the highest number of oral abstracts. Most popular abstract theme was oncology for both poster and oral abstracts. Publications found had in excess of 1250 citations.One hundred and one publications from one hundred and seven conference presentations were identified, distributed across sixty journals. Conference presentation to full publication rate was 18.2%; excluding 2019 conference abstracts the rate was 21.5%. CONCLUSION Publishing research findings is a challenging process. A retrospective analysis of research presented at recent ANZSNM conferences by abstract content was undertaken, with conference presentation to full publication rate found to be at the lower end of reported literature findings.
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Affiliation(s)
- Jo-Anne Pinson
- Radiology Department, Monash Health, Clayton, Victoria, Australia; Radiology Department, Peninsula Health, Frankston, Victoria, Australia; Monash University, Clayton, Victoria, Australia; Department of Health and Human Services, Melbourne, Victoria, Australia
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Contribution of metals to brain MR signal intensity: review articles. Jpn J Radiol 2016; 34:258-66. [PMID: 26932404 DOI: 10.1007/s11604-016-0532-8] [Citation(s) in RCA: 24] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 01/14/2016] [Accepted: 02/18/2016] [Indexed: 12/14/2022]
Abstract
Various metals are essential nutrients in humans, and metal shortages lead to a variety of deficiency diseases. Metal concentration abnormalities may cause metal deposition in the brain, and magnetic resonance imaging (MRI) is the most potent and sensitive technique now available for detecting metal deposition given the difficulties associated with performing brain tissue biopsy. However, the brain contains many kinds of metals that affect the signal intensity of MRI, which has led to numerous misunderstandings in the history of metal analysis. We reviewed the history of brain metal analysis with histologic findings. Typically, manganese overload causes high signal intensity on T1-weighted images (T1WI) in the globus pallidus, iron overload causes low signal intensity in the globus pallidus on T2-weighted images, and gadolinium deposition causes high signal intensity in the dentate nucleus, globus pallidus, and pulvinar of thalamus on T1WI. However, because nonparamagnetic materials and other coexisting metals also affect the signal intensity of brain MRI, the quantitative analysis of metal concentrations is difficult. Thus, when analyzing metal deposition using MRI, caution should be exercised when interpreting the validity and reliability of the obtained data.
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