Showing 11 - 20 of 38 Research Library Publications
Posted: | Victoria Yaneva, Le An Ha, Sukru Eraslan, Yeliz Yesilada, Ruslan Mitkov

Neural Engineering Techniques for Autism Spectrum Disorder: Volume 2, Pages 63-79

 

Automated detection of high-functioning autism in adults is a highly challenging and understudied problem. In search of a way to automatically detect the condition, this chapter explores how eye-tracking data from reading tasks can be used.

Posted: | Karen E. Hauer, Pamela M. Williams, Julie S. Byerley, Jennifer L. Swails, Michael A. Barone

Academic Medicine: Volume 98 - Issue 2 - Pages 162-170

 

The US medical education transition from school to residency is resource-intensive. The Coalition for Physician Accountability aims to improve it, emphasizing learner support, diversity, and minimizing conflicts. This study explores key tensions and offers strategies to align the transition with ideal goals, aiding educators and organizations in implementing recommendations.

Posted: | Christopher Runyon, Polina Harik, Michael Barone

Diagnosis: Volume 10, Issue 1, Pages 54-60

 

This op-ed discusses the advantages of leveraging natural language processing (NLP) in the assessment of clinical reasoning. It also provides an overview of INCITE, the Intelligent Clinical Text Evaluator, a scalable NLP-based computer-assisted scoring system that was developed to measure clinical reasoning ability as assessed in the written documentation portion of the now-discontinued USMLE Step 2 Clinical Skills examination. 

Posted: | Hanin Rashid, Christopher Runyon, Jesse Burk-Rafel, Monica M. Cuddy, Liselotte Dyrbye, Katie Arnhart, Ulana Luciw-Dubas, Hilit F. Mechaber, Steve Lieberman, Miguel Paniagua

Academic Medicine: Volume 97 - Issue 11S - Page S176

 

As Step 1 begins to transition to pass/fail, it is interesting to consider the impact of score goal on wellness. This study examines the relationship between goal score, gender, and students’ self-reported anxiety, stress, and overall distress immediately following their completion of Step 1.

Posted: | Erfan Khalaji, Sukru Eraslan, Yeliz Yesilada, Victoria Yaneva

Behavior & Information Technology

 

This study builds upon prior work in this area that focused on developing a machine-learning classifier trained on gaze data from web-related tasks to detect ASD in adults. Using the same data, we show that a new data pre-processing approach, combined with an exploration of the performance of different classification algorithms, leads to an increased classification accuracy compared to prior work.

Posted: | Jonathan D. Rubright, Thai Q. Ong, Michael G. Jodoin, David A. Johnson, Michael A. Barone

Academic Medicine: Volume 97 - Issue 8 - Pages 1219-1225

 

Since 2012, the United States Medical Licensing Examination (USMLE) has maintained a policy of ≤ 6 attempts on any examination component. The purpose of this study was to empirically examine the appropriateness of existing USMLE retake policy.

Posted: | Monica M. Cuddy, Chunyan Liu, Wenli Ouyang, Michael A. Barone, Aaron Young, David A. Johnson

Academic Medicine: June 2022

 

This study examines the associations between Step 3 scores and subsequent receipt of disciplinary action taken by state medical boards for problematic behavior in practice. It analyzes Step 3 total, Step 3 computer-based case simulation (CCS), and Step 3multiple-choice question (MCQ) scores.

Posted: | Daniel Jurich, Chunyan Liu, Amanda Clauser

Journal of Graduate Medical Education: Volume 14, Issue 3, Pages 353-354

 

Letter to the editor.

Posted: | Andrew A. White, Ann M. King, Angelo E. D’Addario, Karen Berg Brigham, Suzanne Dintzis, Emily E. Fay, Thomas H. Gallagher, Kathleen M. Mazor

JMIR Medical Education: Volume 8 - Issue 2 - e30988

 

This article aims to compare the reliability of two assessment groups (crowdsourced laypeople and patient advocates) in rating physician error disclosure communication skills using the Video-Based Communication Assessment app.

Posted: | Jonathan D. Rubright, Michael Jodoin, Stephanie Woodward, Michael A. Barone

Academic Medicine: Volume 97 - Issue 5 - Pages 718-722

 

The purpose of this 2019–2020 study was to statistically identify and qualitatively review USMLE Step 1 exam questions (items) using differential item functioning (DIF) methodology.