Real-World Data: Applications and Relevance to Cancer Clinical Trials is a research paper published in Seminars in Radiation Oncology (2023). On theSindex it has a DataRank of 0.457. It has been cited 20 times.
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Base Score Contribution
0.457
From this paper's citation signal
Citation Network Contribution
0
Citation network not refreshed for this result
This paper's DataRank is currently driven only by its base citation score. Citation network data was not refreshed for this result.
Learn more about DataRank methodology →National Institutes of Health
Grant: 2018-2022
National Institutes of Health
Grant: LRP 1 L30 CA231572-01
American Cancer Society
Grant: CSDG-20-013-01-CCE
NCI NIH HHS
Grant: L30 CA231572
FWCI
6.28
Citation Percentile
1.0%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 1 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 2 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 2 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 3 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 3 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 4 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study
Additional file 4 of Identification of prognostic signatures in remnant gastric cancer through an interpretable risk model based on machine learning: a multicenter cohort study