This review was written by Anna Blenda, PhD, a member of the IAMSE Publications Committee, and is from the February 2021 issue of Medical Science Educator. The article is titled “A Novel USMLE Step 1 Projection Model Using a Single Comprehensive Basic Science Self-Assessment Taken During a Brief Intense Study Period” and was written by Stephen D. Bigach, Robert D. Winkelman, Jonathan C. Savakus & Klara K. Papp. Med.Sci.Educ. 31, pages67–73(2021).
For a significant number of years, US medical students have been spending most of their first two years mastering a basic sciences curriculum, as well as preparing to apply this knowledge in the United States Medical Licensing Examination (USMLE) Step 1 examination. Bigach et al. rightfully point out that this Step 1 exam historically has been a high stakes affair, scores being used more and more frequently to rank applicants for the residency interview and match process. This is especially true for highly competitive residency specialties, and medical students have been very much aware of the use of these scores for unofficial ‘screening’.
Thus, prediction of the student performance on Step 1 has always been of high importance, with scholarly attempts to develop methods to increase the accuracy of Step 1 score prediction. However, very few prediction models have been so far validated in the published medical education literature.
The increased student anxiety over the score outcome of Step 1 quite often leads to the re-scheduling of the initial exam date to the later date(s), involving additional fees increasing proportionally to the increased time of delay. As a standard, Comprehensive Basic Science Self-Assessments (CBSSAs) by the National Board of Medical Examiners (NBME) are used by students to evaluate their preparedness for the Step 1 exam and make a decision about possible rescheduling of the initial exam date. However, it is difficult for students to predict the optimal amount of additional time needed to achieve a certain desired exam score.
The novelty and usefulness of the predictive model proposed by Bigach et al. is that, based on just a single CBSSA exam, it provides students with not one, but a range of projected Step 1 scores relative to the number of days out from Step 1 exam that CBSSA was taken under test-like conditions. The authors present validated results from a two-year recent (2016 and 2017) study. This is the first published validated prediction model, which gives a range of projected Step 1 scores as a function of days out from the Step 1 date. This information will be extremely valuable to students for evaluating their exam readiness and scheduling the Step 1 exam date accordingly.
The authors fairly indicate the limitations of the proposed predictive model, considering the cohort consisted of only high-performing students representing a single US medical school. The study has been internally validated, but it has not been externally validated. In addition, this predictive model was not intended for students trying to predict the passage of the Step 1 exam. Nonetheless, the proposed model is definitely a valuable tool for predicting the score of Step 1 from a single CBSSA exam. In addition, with upcoming implementation of pass/fail Step 1 scoring, this model could be used as a foundation for further assessment and research directions.
Anna V. Blenda, PhD
Department of Biomedical Sciences
University of South Carolina School of Medicine Greenville
Prisma Health Cancer Institute