Abstract
MD-PhD programs prepare physicians for research-focused careers. The challenge for admissions committees is to select from among their applicants those who will achieve this goal, becoming leaders in academic medicine and biomedical research. Although holistic practices are encouraged, the temptation remains to use metrics such as grade point average, Medical College Admission Test scores, and postbaccalaureate gap length, combined with race and ethnicity, age at college graduation, and sex to select whom to interview and admit. Here, we asked whether any of these metrics predict performance in training or career paths after graduation. Data were drawn from the National MD-PhD Program Outcomes Study with information on 4,659 alumni and 593 MD-PhD graduates of the Albert Einstein College of Medicine and the University of Pennsylvania. The Penn-Einstein dataset included admissions committee summative scores, attrition, and the number and impact of PhD publications. Output metrics included time to degree, eventual employment in workplaces consistent with MD-PhD training goals, and self-reported research effort. Data were analyzed using machine learning and multivariate linear regression. The results show that none of the applicant metrics, individually or collectively, predicted in-program performance, future research effort, or eventual workplace choices even when comparisons were limited to those in the top and bottom quintiles.
Authors
Lawrence F. Brass, Maurizio Tomaiuolo, Aislinn Wallace, Myles H. Akabas
Figure 4
National dataset applicant metrics do not predict outcomes after completion of postgraduate training including favorable workplace or self-reported research effort for graduates in favorable workplaces, and the more extensive applicant metrics for the Penn-Einstein dataset, including publications during PhD, do not predict favorable workplace following completion of postgraduate training.
