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A Novel USMLE Step 1 Projection Model Using a Single Comprehensive Basic Science Self-Assessment Taken During a Brief

Stephen D Bigach1,2, Robert D Winkelman2, Jonathan C Savakus2,3

  • 1Department of Orthopaedic Surgery, McGaw Medical Center of Northwestern University, IL Chicago, USA.

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|August 30, 2021
PubMed
Summary
This summary is machine-generated.

A new model predicts United States Medical Licensing Examination (USMLE) Step 1 scores using Comprehensive Basic Science Self-Assessments (CBSSAs). This tool helps medical students optimize study plans and gauge exam readiness.

Keywords:
Projection modelingStep 1TestingUSMLE

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Area of Science:

  • Medical Education
  • Standardized Testing
  • Student Assessment

Background:

  • Comprehensive Basic Science Self-Assessments (CBSSAs) are utilized by medical students to evaluate their preparedness for the United States Medical Licensing Examination (USMLE) Step 1.
  • High USMLE Step 1 scores are valued by residency programs, prompting students to invest significant resources in exam preparation.
  • There is a need for tools to help students effectively plan their testing and study strategies.

Purpose of the Study:

  • To develop a predictive model for USMLE Step 1 scores based on a single CBSSA.
  • To provide students with a tool to estimate their performance several days before the actual exam.
  • To inform student testing and study plans by offering a predicted score.

Main Methods:

  • A survey of USMLE Step 1 test-takers from a single US medical school in 2016 and 2017.
  • Calculation of average daily score improvement from CBSSA to Step 1 in 2016 to create a predictive model.
  • Validation of the predictive model using 2017 data, calculating mean absolute prediction errors (MAPEs).

Main Results:

  • A prediction model was developed: [Predicted Step 1 = 292 - (292 - CBSSA score) * 0.987527 ^ (number of days out)].
  • In 2016, the mean absolute prediction error (MAPE) was 7.8; in 2017, it was 7.7.
  • In 2017, 72.1% and 96.7% of actual Step 1 scores fell within one and two standard deviations of the predicted score, respectively.

Conclusions:

  • The developed projection model offers a reasonable method for students to assess their USMLE Step 1 readiness.
  • This model provides a valuable framework for creating future predictive tools as standardized testing evolves in medical education.
  • The model is useful while the USMLE Step 1 remains a scored examination.