Identifying unique profiles of perceived dyspnea burden in heart failure is a research paper published in Heart & Lung (2020). On theSindex it has a DataRank of 0.477. It has been cited 23 times.
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Base Score Contribution
0.477
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 →NCATS NIH HHS
Grant: UL1 TR002369
NINR NIH HHS
Grant: R01 NR013492
NINR NIH HHS
Grant: F31 NR015936
NCATS NIH HHS
Grant: UL1 TR000128
American Heart Association
National Institute of Child Health and Human Development
National Center for Advancing Translational Sciences
National Institutes of Health
FWCI
1.79
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords
Sustainable Development Goals
Additional file 1 of Do patient-reported outcome measures measure up? A qualitative study to examine perceptions and experiences with heart failure proms among diverse, low-income patients
Additional file 1 of Do patient-reported outcome measures measure up? A qualitative study to examine perceptions and experiences with heart failure proms among diverse, low-income patients
Additional file 2 of Do patient-reported outcome measures measure up? A qualitative study to examine perceptions and experiences with heart failure proms among diverse, low-income patients
Additional file 2 of Do patient-reported outcome measures measure up? A qualitative study to examine perceptions and experiences with heart failure proms among diverse, low-income patients
Additional file 1 of Distinct implications of body mass index in different subgroups of nonobese patients with heart failure with preserved ejection fraction: a latent class analysis of data from the TOPCAT trial
Additional file 1 of Distinct implications of body mass index in different subgroups of nonobese patients with heart failure with preserved ejection fraction: a latent class analysis of data from the TOPCAT trial