Do Articles Shared by Academic Medicine Social Media Influencers Drive Future Citation Rates?

OBJECTIVE: To assess the role of influential figures within social media (SoMe) in driving future citations. METHODS: All original articles published in the Journal of Urology and European Urology in 2018 were identified. For each article, number of mentions on any SoMe platform, article's Twitter reach, and total citations were collected. Article characteristics such as type of study, article topic, and open access status were identified. Total academic research output was obtained for first and last authors of included articles. Influential SoMe figures were defined as users that tweeted about included articles and had over 2000 followers. For these accounts, we collected total followers, total tweets, engagement statistics, verification status, and academic characteristics such as total citations and total prior publications. The impact of SoMe, article, and academic characteristics on future citations was assessed using panel data regression analysis. RESULTS: We identified 394 articles with 8895 total citations and 460 SoMe influencers. On panel data regression modeling, tweets about a specific article were associated with future citations (0.17 citations per tweet about an article, P < .001). SoMe influencer characteristics were not associated with increased citations (P > .05). The following non-SoMe-associated characteristics were predictive of future citations (P < .001): study type (prospective studies received 12.9 more citations than cross-sectional studies), open access status (4.3 citations more if open access, P < .001), and previously well-published first and last authors. CONCLUSION: While SoMe posts are associated with increased visibility and higher future citation rates, SoMe influencers do not appear to drive these outcomes. Instead, high quality and accessibility were more predictive of future citability.

The COVID-19 Residency Application Cycle Did Not Affect Geographic Dispersal Patterns Among Applicants Entering the Urology Match: A Quantitative Mapping Study.

OBJECTIVE: To assess whether pandemic-related restrictions with video-based interviewing increased geographic clustering of urology applicants matching at a residency program near where they were raised or attended medical school. MATERIALS AND METHODS: We utilized publicly available data from the urology match to compare dispersal patterns between applicants matching during the COVID-19 application cycle (2021) and those matching in the 5 prior application cycles (2016-2020). Variables included home state, undergraduate institution, medical school, and residency. Latitudes and longitudes were obtained for each institution and home state. The primary endpoint was distance (miles, as the most direct path) between medical school and residency program. We also assessed dispersal patterns by American Urological Association section. RESULTS: Of the 1965 applicants matching to a urology program between 2016 and 2021, medical school was identified for 1956 (99.7%) applicants, undergraduate program for 1551 (79%) applicants, and home state for 1351 (69%) applicants. Comparing the COVID-19 application cycle to the 5 prior application cycles, there was no significant difference in the median distance between medical school and residency, undergraduate university and residency, or home state and residency. Similarly, there was no significant difference in the proportion of applicants matching at their home institution, matching from medical schools without a home urology program, matching from medical schools with a historically low volume of urology applicants (<1 matched applicant per year), or matching from a D.O. CONCLUSION: Virtual interviewing and the loss of in-person clinical rotations did not significantly alter dispersal patterns or hamper an applicant's ability to match at program outside their geographic region.