J
Jordan D. Dworkin
Researcher at Columbia University
Publications - 45
Citations - 1199
Jordan D. Dworkin is an academic researcher from Columbia University. The author has contributed to research in topics: Medicine & Internal medicine. The author has an hindex of 12, co-authored 29 publications receiving 470 citations. Previous affiliations of Jordan D. Dworkin include Columbia University Medical Center & University of Pennsylvania.
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Journal ArticleDOI
Gendered Citation Practices in the Field of Communication.
X. Wang,Jordan D. Dworkin,Dale Zhou,Jennifer Stiso,Emily B. Falk,Danielle S. Bassett,Perry Zurn,David M. Lydon-Staley,David M. Lydon-Staley +8 more
TL;DR: This paper found that citations of women as first and last authors attract fewer citations than papers with men in those positions than would be expected if gender were unrelated to referencing, and the imbalance is driven largely by the citation practices of men and is slowly decreasing over time.
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In)citing Action to Realize an Equitable Future
TL;DR: This work discusses relevant ethical considerations and offers practical recommendations to scientists of all ages about how to ensure an equitable future by all scientists for all scientists.
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Data interpretation in analgesic clinical trials with statistically nonsignificant primary analyses: an ACTTION systematic review.
Jennifer S. Gewandter,Andrew McKeown,Michael P. McDermott,Jordan D. Dworkin,Shannon M. Smith,Robert A. Gross,Matthew Hunsinger,Allison H. Lin,Bob A. Rappaport,Andrew S.C. Rice,Michael C. Rowbotham,Mark R. Williams,Dennis C. Turk,Robert H. Dworkin +13 more
TL;DR: There is a need for authors, reviewers, and editors to be more cognizant of how analgesic RCT results are presented and attempt to minimize spin in future clinical trial publications.
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Bayesian nonparametric generative models for causal inference with missing at random covariates
TL;DR: A general Bayesian nonparametric (BNP) approach to causal inference in the point treatment setting that is well‐suited for causal inference problems, as it does not require parametric assumptions about the distribution of confounders and naturally leads to a computationally efficient Gibbs sampling algorithm.
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Structural brain measures among children with and without ADHD in the Adolescent Brain and Cognitive Development Study cohort: a cross-sectional US population-based study.
TL;DR: In this paper , the authors used linear mixed effects models to estimate Cohen's d values associated with ADHD for 79 brain measures of cortical thickness, cortical area, and subcortical volume.