Identifying low acuity Emergency Department visits with a machine learning approach: The low acuity visit algorithms (LAVA) is a dataset published in Health Services Research (2024). On theSindex it has a DataRank of 0.277, placing it in the top 57.1% of the data-sharing corpus. It has been cited 4 times, with 3 citing works in its 1-hop citation network.
Ranks in the top 57% for downstream scientific impact
DataRank reads this dataset's downstream impact straight off the citation graph — no black box, no proprietary weighting. How is this computed?
FAIR checklist signals are shown for context only and do not affect DataRank scoring.
We only score data papers we can read in full — never from an abstract alone.
Base Score Contribution
0.241
From this paper's citation signal
Citation Network Contribution
0.0356
From 2 citing papers with measurable signal
Ranked by each citer's contribution to N(p) — log1p(Cq) divided by its reference count — out of 3 citers.
National Institute on Aging
Grant: 1R03AG078933‐01
National Institute on Aging
Grant: T32 AG051090
NIA NIH HHS
Grant: 1R03AG078933-01
NIA NIH HHS
Grant: R03 AG078933
National Institutes of Health
Grant: 5T32AG051090-05
Training in Healthcare Financing, Organization and Delivery for Aging Populations
National Institutes of Health
Grant: 5R03AG078933-02
Trajectories of Frailty and Cognitive Impairment in Older Adults
Wharton School, University of Pennsylvania (Wharton AI and Analytics for Business)
Wharton School, University of Pennsylvania
FWCI
2.18
Citation Percentile
0.9%
Citation Trend
Fields of Study
MeSH Terms
Keywords