School of Medicine and Health Sciences Poster Presentations
Poster Number
135
Document Type
Poster
Keywords
Big data; Oncology; Publication
Publication Date
3-2016
Abstract
Background: The American Society of Clinical Oncology launched CancerLinQ project in 2010 to provide real-time data collection, mining and visualization, clinical decision support, and quality feedback. Creation of a big data software platform is currently underway to power the CancerLinQ in the phase II of the project. This would allow for evidence driven practice and rapid learning for cancer care providers. Additionally, adequate knowledge about the utility of Big Data to encourage provider utilization in high Impact Factor (IF) journals is needed. We aimed to assess trends and quality of Big Data published in Oncology.
Methods: Peer-reviewed English papers published between 2011 and 2015 reporting on cancer and Big Data were identified using PubMed. Manual review was conducted. Cohort construction and statistical analyses were performed utilizing SPSS v 21.0
Results: We identified 325 publications, 135 met inclusion criteria in 105 journals, of which 36% (n=38) are considered specialized hematology and/or oncology journals. Specialized journals published 29.62% (40/135). Equal distribution of publications was found in clinical and basic science journals; 54 (37%) and 50 (40%) respectively. There was a trend of increased publications in clinical journals from 2012 to 2015 (16.7% to 42.9%, P = 0.39). Of the available Impact factors (IF) – the median is 3.234 (range 0.00-41.456) with 25/125 (20.0%) of available IF being > 5.00 and 12/125 (9.6%) being > 10.00 with no difference in the proportion of IF > 5.00 in clinical versus basic science journals; 11/51 (21%) versus 11/47 (23) % p = 1.00, respectively.
Conclusions: The need for further publication of studies addressing Big Data use in furthering oncology research is being met by the research community in response to the CancerLinQ as demonstrated by the rapid increase in publications. We hypothesize that this will increase the likelihood of cancer providers using CancerLinQ in the future, although an increase in publication in specialized journals and in those with high impact factors is still necessary. Currently, despite the increased trend of publications addressing Big Data in oncology, less than one-third of these publications are in specialized journals.
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Open Access
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Is the Force Awakened? Publication Trends in Oncology Big Data as Phase II CancerLinQ is Launched
Background: The American Society of Clinical Oncology launched CancerLinQ project in 2010 to provide real-time data collection, mining and visualization, clinical decision support, and quality feedback. Creation of a big data software platform is currently underway to power the CancerLinQ in the phase II of the project. This would allow for evidence driven practice and rapid learning for cancer care providers. Additionally, adequate knowledge about the utility of Big Data to encourage provider utilization in high Impact Factor (IF) journals is needed. We aimed to assess trends and quality of Big Data published in Oncology.
Methods: Peer-reviewed English papers published between 2011 and 2015 reporting on cancer and Big Data were identified using PubMed. Manual review was conducted. Cohort construction and statistical analyses were performed utilizing SPSS v 21.0
Results: We identified 325 publications, 135 met inclusion criteria in 105 journals, of which 36% (n=38) are considered specialized hematology and/or oncology journals. Specialized journals published 29.62% (40/135). Equal distribution of publications was found in clinical and basic science journals; 54 (37%) and 50 (40%) respectively. There was a trend of increased publications in clinical journals from 2012 to 2015 (16.7% to 42.9%, P = 0.39). Of the available Impact factors (IF) – the median is 3.234 (range 0.00-41.456) with 25/125 (20.0%) of available IF being > 5.00 and 12/125 (9.6%) being > 10.00 with no difference in the proportion of IF > 5.00 in clinical versus basic science journals; 11/51 (21%) versus 11/47 (23) % p = 1.00, respectively.
Conclusions: The need for further publication of studies addressing Big Data use in furthering oncology research is being met by the research community in response to the CancerLinQ as demonstrated by the rapid increase in publications. We hypothesize that this will increase the likelihood of cancer providers using CancerLinQ in the future, although an increase in publication in specialized journals and in those with high impact factors is still necessary. Currently, despite the increased trend of publications addressing Big Data in oncology, less than one-third of these publications are in specialized journals.
Comments
Presented at: GW Research Days 2016