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    This study developed a model to predict resident milestone levels using entrustable professional activities (EPAs) in pediatric residency programs. The model accurately forecasts milestone progression, aiding in resident assessment.

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    Area of Science:

    • Medical Education
    • Graduate Medical Training Assessment

    Background:

    • Residency programs increasingly require assessment using Accreditation Council for Graduate Medical Education (ACGME) milestones and specialty-defined entrustable professional activities (EPAs).
    • Accurate assessment is crucial for resident development and program evaluation.

    Purpose of the Study:

    • To develop and validate a predictive model for individual resident milestone levels.
    • To utilize entrustment-supervision levels of EPAs as predictors for ACGME milestone achievement.

    Main Methods:

    • A prospective cohort study involving 48 U.S. pediatric residency programs over 3 academic years (2021-2024).
    • Multilevel structural equation models were fitted using biannual EPA entrustment-supervision and ACGME milestone data from 4,328 residents.
    • Two models were developed: one using 17 EPAs and another using 12 EPAs.

    Main Results:

    • The models demonstrated excellent internal fit (comparative fit indexes > 0.98).
    • External prediction of milestone levels for future cycles showed strong correlations (coefficients ranging from 0.68 to 0.72).
    • Both 17-EPA and 12-EPA models exhibited similar predictive capabilities.

    Conclusions:

    • A robust model exists to predict resident milestone levels from EPA entrustment-supervision data.
    • This predictive capability supports the meaningful integration of EPAs and milestones in residency program assessments.
    • The findings facilitate more effective and data-driven resident evaluation strategies.