Evaluating eligibility criteria of oncology trials using real-world data and AI is a research paper published in Nature (2021). On theSindex it has a DataRank of 12.4. It has been cited 318 times, with 200 citing works in its 1-hop citation network.
Scored on demand from live citation data
Linked data & code
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Base Score Contribution
0.865
From this paper's citation signal
Citation Network Contribution
11.5
From 200 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 200 citers.
NCATS NIH HHS
Grant: UL1 TR003142
NIA NIH HHS
Grant: P30 AG059307
NCI NIH HHS
Grant: P30 CA124435
National Institutes of Health
Grant: 3P30CA124435-08S1
Stanford University Cancer Center
National Institutes of Health
Grant: 5UL1TR003142-03
Stanford Center for Clinical & Translational Education and Research (Spectrum)
FWCI
15.27
Citation Percentile
1.0%
Influential Citations
14
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
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Additional file 2 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research
Additional file 4 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research
Additional file 4 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research
Additional file 6 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research
Additional file 6 of Validating and automating learning of cardiometabolic polygenic risk scores from direct-to-consumer genetic and phenotypic data: implications for scaling precision health research
Additional file 1 of More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study
Additional file 1 of More efficient and inclusive time-to-event trials with covariate adjustment: a simulation study