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RESEARCH LIBRARY

View the latest publications from members of the NBME research team

Showing 1 - 6 of 6 Research Library Publications
Posted: | Stanley J. Hamstra, Monica M. Cuddy, Daniel Jurich, Kenji Yamazaki, John Burkhardt, Eric S. Holmboe, Michael A. Barone, Sally A. Santen

Academic Medicine: Volume 96 - Issue 9 - Pages 1324-1331

 

This study examines associations between USMLE Step 1 and Step 2 Clinical Knowledge (CK) scores and ACGME emergency medicine (EM) milestone ratings.

Posted: | Katie L. Arnhart, Monica M. Cuddy, David Johnson, Michael A. Barone, Aaron Young

Academic Medicine: Volume 96 - Issue 9 - Pages 1319-1323

 

This study examined the relationship between USMLE attempts and the likelihood of receiving disciplinary actions from state medical boards.

Posted: | J. Salt, P. Harik, M. A. Barone

Academic Medicine: July 2019 - Volume 94 - Issue 7 - p 926-927

 

A response to concerns regarding potential bias in the implementation of machine learning (ML) to scoring of the United States Medical Licensing Examination Step 2 Clinical Skills (CS) patient notes (PN).

Posted: | D. Jurich, M. Daniel, M. Paniagua, A. Fleming, V. Harnik, A. Pock, A. Swan-Sein, M. A. Barone, S.A. Santen

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 371-377

 

Schools undergoing curricular reform are reconsidering the optimal timing of Step 1. This study provides a psychometric investigation of the impact on United States Medical Licensing Examination Step 1 scores of changing the timing of Step 1 from after completion of the basic science curricula to after core clerkships.

Posted: | J. Salt, P. Harik, M. A. Barone

Academic Medicine: March 2019 - Volume 94 - Issue 3 - p 314-316

 

The United States Medical Licensing Examination Step 2 Clinical Skills (CS) exam uses physician raters to evaluate patient notes written by examinees. In this Invited Commentary, the authors describe the ways in which the Step 2 CS exam could benefit from adopting a computer-assisted scoring approach that combines physician raters’ judgments with computer-generated scores based on natural language processing (NLP).

Posted: | M. Paniagua, P. Katsufrakis

Investigación en Educación Médica, Vol. 8, Núm. 29, 2019