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Inverse Probability of Treatment Weighting (Propensity Score) using the Military Health System Data Repository and National Death Index
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Contemporary Step 1 Predictive Methods Across 12 US MD-Granting Medical Schools.

Christian C Steciuch1, James H Baños2, Todd A Bates3

  • 1School of Medicine, University of Kansas, Kansas City, Kansas, USA.

Teaching and Learning in Medicine
|April 13, 2026
PubMed
Summary
This summary is machine-generated.

Following the USMLE Step 1 scoring change, failure rates increased. This study compared 12 medical schools' methods for identifying at-risk students, finding NBME exams most predictive, but varied intervention strategies persist.

Keywords:
USMLE Step 1predictive modelingstudent success

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Area of Science:

  • Medical Education
  • Assessment and Evaluation
  • Educational Data Mining

Background:

  • The United States Medical Licensing Examination (USMLE) Step 1 scoring shifted to pass/fail in 2022, leading to increased national first-time failure rates.
  • This change necessitates improved, data-driven methods for identifying medical students at risk of failing Step 1 to enable timely interventions.
  • Medical school structures and assessment methods vary, complicating the development of universally applicable risk identification models.

Purpose of the Study:

  • To compare the predictive models used by 12 US MD-granting medical schools to identify students at risk of failing the USMLE Step 1 exam.
  • To assess the sensitivity, specificity, and timing of risk identification across different predictive methodologies.
  • To identify key features and provide recommendations for developing, implementing, and refining Step 1 predictive models.

Main Methods:

  • A comparative analysis of 12 US medical schools' USMLE Step 1 predictive models.
  • Data collection involved virtual meetings and surveys detailing each institution's risk identification methods.
  • Models were evaluated based on their ability to predict Step 1 failure, including sensitivity, specificity, and the timing of risk identification relative to the exam date.

Main Results:

  • Six institutions used categorical risk assessment, three used multiple regression, two combined approaches, and one used growth mixture modeling.
  • Performance on National Board of Medical Examiners (NBME) Comprehensive Basic Science Examination (CBSE) or Comprehensive Basic Science Self-Assessment (CBSSA) exams were the most significant predictors across institutions.
  • Most schools identified at-risk students at the beginning of their dedicated Step 1 study period, with varying intervention thresholds.

Conclusions:

  • Contemporary USMLE Step 1 predictive models exhibit diverse methodologies with inherent strengths and limitations.
  • While NBME exams are strong predictors, the effectiveness of interventions varies due to differing school thresholds.
  • Continuous improvement of predictive model performance is crucial for enhancing student learning outcomes and success on the USMLE Step 1.