Impact of preadmission variables on USMLE step 1 and step 2 performance

James Kleshinski, Sadik A. Khuder, Joseph I. Shapiro, Jeffrey P. Gold

Research output: Contribution to journalArticlepeer-review

59 Scopus citations


Purpose To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. Method Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of performance on step 1 and step 2. A generalized regression neural network (GRNN) as well as a feed forward neural network (FFNN) was then developed in an effort to more accurately predict step 1 and step 2 scores from these preadmission data. Results Statistically significant predictors for step 1 and step 2 included science grade point average (SGPA), the biologic science (BS) section of the Medical College Admissions Test (MCAT), college selectivity, race, and age of the applicant. Neural networks were found to predict a significant portion of the variance, and the FFNN demonstrated some superiority over that obtained with linear regression models as well as the GRNN. Conclusions The results have implications that could impact the selection of applicants to medical school and the neural networks that we developed could be used in a prospective manner.

Original languageEnglish (US)
Pages (from-to)69-78
Number of pages10
JournalAdvances in Health Sciences Education
Issue number1
StatePublished - Mar 2009
Externally publishedYes


  • Medical school admissions
  • Neural network

ASJC Scopus subject areas

  • Education


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