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    Specific keywords in resident evaluations can predict failure. An algorithm using classification and regression trees identified at-risk trainees, aiding early intervention in medical education.

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    Area of Science:

    • Medical Education Research
    • Competency-Based Assessment
    • Residency Training

    Background:

    • Literature suggests keywords in summative rotation assessments may indicate abnormal progress or failure.
    • Early identification of residents at risk is crucial for timely intervention and support.

    Purpose of the Study:

    • To determine the relationship between specific keywords in in-training evaluation reports (ITERs) and subsequent abnormal progress or failure.
    • To develop a functional algorithm for identifying residents at risk of failure.

    Main Methods:

    • A database of 41,618 ITERs from 3,292 residents (2001-2013) at Université Laval was analyzed.
    • An instructional designer identified keywords associated with feedback and underperformance.
    • A classification and regression tree algorithm was constructed and tuned for 100% sensitivity.

    Main Results:

    • Failure to progress was detected in 6% of family medicine residents and 4% of residents in 36 other specialties.
    • Positive predictive values for failure were approximately 23.3% for family medicine and 23.4% for other specialties.
    • Low positive predictive values may reflect performance improvement or reluctance to assign failing scores.

    Conclusions:

    • Classification and regression trees can identify keywords for risk assessment algorithms.
    • An algorithm can be implemented in electronic assessment systems to detect residents at risk of poor performance.
    • This approach supports proactive interventions to improve resident outcomes.