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Predicting academic performance in surgical training.

Michael J Yost¹, Jeffery Gardner¹, Richard McMurtry Bell²

¹Department of Surgery, Medical University of South Carolina, Charleston, South Carolina.

Journal of Surgical Education

|January 21, 2015

Summary

This summary is machine-generated.

Identifying at-risk surgical residents early through behavioral and motivational assessments can improve American Board of Surgery In-Training Examination (ABSITE) performance. Proactive intervention by program directors enhances resident success and training program outcomes.

Keywords:

ABSITE Interpersonal and Communication Skills Medical Knowledge Professionalism Systems-Based Practice TriMetrix in-training examination predicting academic success

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Area of Science:

Medical Education
Surgical Training
Psychometrics in Medicine

Background:

Surgical residency programs evaluate trainees on 6 ACGME competencies.
Program success is partly measured by graduate pass rates on American Board of Surgery (ABS) qualifying and certification exams.
The ABSITE is used as a predictor of success on ABS certification exams, with remediation for poor performance being resource-intensive.

Purpose of the Study:

To explore if resident behavioral and motivational characteristics can identify individuals at risk for substandard ABSITE performance.
To enable program directors (PDs) to proactively tailor curricula for at-risk residents.
To improve resident success rates and reduce the need for costly remediation.

Main Methods:

Seven surgical training programs participated, with residents voluntarily completing an online assessment of behavioral style, motivators, and Acumen Index (TriMetrix).
Resident assessments were anonymized using code names and linked to 2013 ABSITE scores.
Data were analyzed using logistic regression and a neural network (NN) to model performance prediction.

Main Results:

Analysis of 117 residents (64 senior, 53 junior) showed significant correlations between TriMetrix parameters and ABSITE pass/fail rates (≥70% passing).
For senior residents, higher 'Theoretical' scores correlated with a lower likelihood of passing (p < 0.043).
For junior residents, higher 'Internal Role Awareness' scores correlated with an increased likelihood of passing (p < 0.004); the NN model showed high predictive accuracy for both groups.

Conclusions:

Behavioral, motivational, and acumen characteristics are valuable indicators for identifying residents at risk for poor ABSITE performance.
Program directors can use this information for proactive, individualized interventions to enhance resident success.
The developed neural network model shows promise for predicting examination performance, warranting further validation.