Showing 1 - 10 of 13 Research Library Publications
Posted: | John Norcini, Irina Grabovsky, Michael A. Barone, M. Brownell Anderson, Ravi S. Pandian, Alex J. Mechaber

Academic Medicine: Volume 99 - Issue 3 - p 325-330

 

This retrospective cohort study investigates the association between United States Medical Licensing Examination (USMLE) scores and outcomes in 196,881 hospitalizations in Pennsylvania over 3 years.

Posted: | Victoria Yaneva, Peter Baldwin, Daniel P. Jurich, Kimberly Swygert, Brian E. Clauser

Academic Medicine: Volume 99 - Issue 2 - p 192-197

 

This report investigates the potential of artificial intelligence (AI) agents, exemplified by ChatGPT, to perform on the United States Medical Licensing Examination (USMLE), following reports of its successful performance on sample items. 

Posted: | Thai Ong, Becky Krumm, Margaret Wells, Susan Read, Linda Harris, Andrea Altomare, Miguel Paniagua

Academic Medicine: Volume 99 - Issue 7 - Pages 778-783

 

This study examined score comparability between in-person and remote proctored administrations of the 2020 Internal Medicine In-Training Examination (IM-ITE) during the COVID-19 pandemic. Analysis of data from 27,115 IM residents revealed statistically significant but educationally nonsignificant differences in predicted scores, with slightly larger variations observed for first-year residents. Overall, performance did not substantially differ between the two testing modalities, supporting the continued use of remote proctoring for the IM-ITE amidst pandemic-related disruptions.

Posted: | M. von Davier, YS. Lee

Springer International Publishing; 2019

 

This handbook provides an overview of major developments around diagnostic classification models (DCMs) with regard to modeling, estimation, model checking, scoring, and applications. It brings together not only the current state of the art, but also the theoretical background and models developed for diagnostic classification.

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: | R.A. Feinberg, D.P Jurich

On the Cover. Educational Measurement: Issues and Practice, 38: 5-5

 

This informative graphic reports between‐individual information where a vertical line—with dashed lines on either side indicating an error band—spans three graphics allowing a student to easily see their score relative to four defined performance categories and, more notably, three relevant score distributions.

Posted: | P. M. Wallach L. M. Foster, M. M. Cuddy, M. M. Hammoud, K. Z. Holtzman, D. B. Swanson

J Gen Intern Med 34, 705–711 (2019)

 

This study examines medical student accounts of EHR use during their internal medicine (IM) clerkships and sub-internships during a 5-year time period prior to the new clinical documentation guidelines.

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: | E. Knetka, C. Runyon, S. Eddy

CBE—Life Sciences Education Vol. 18, No. 1

 

This article briefly reviews the aspects of validity that researchers should consider when using surveys. It then focuses on factor analysis, a statistical method that can be used to collect an important type of validity evidence.

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).