Association of residency application data with subsequent general surgery residency graduate performance.

Background: When selecting surgical residents, programs emphasize quantifiable data from the Electronic Residency Application Service application. However, it is unclear whether Electronic Residency Application Service data are associated with future resident performance or any of the qualities (surgical judgment, leadership, and medical knowledge) that our group has identified as being predictive of graduate performance. Our objective was to determine whether residency application variables are associated with subsequent residency graduate performance as rated by surgical educators.

Methods: Faculty from 12 general surgery residency programs rated graduates from 2017 to 2020 on 4 outcomes: overall performance, surgical judgment, leadership, and medical knowledge. Graduates' Electronic Residency Application Service data were collected, including medical school type, United States Medical Examination scores, honors society memberships, extracurriculars, clerkship honors, and class rank. Data were analyzed using the Spearman rank-order correlation. Least absolute shrinkage and selection operator regression was performed to select a model predictive of each outcome from preresidency variables.

Results: A total of 258 graduates were evaluated. Regarding overall residency graduate performance rating, there were weak associations with the United States Medical Examination step 2 score (r = 0.23, P < .01); honors in family medicine (r = 0.17, P = .02), obstetrics/gynecology (r = 0.17, P = .01), pediatrics (r = 0.15, P = .02), and surgery (r = 0.14, P = .03); proportion of clerkship honors (r = 0.2, P < .01); and class rank (r = 0.18, P = .03). None of the preresidency variables were selected for a predictive model via least absolute shrinkage and selection operator regression for any of the 4 outcomes measured.

Conclusion: There is a weak correlation between measurable residency application data and subsequent resident performance as rated by surgical educators. On least absolute shrinkage and selection operator regression, no residency application variables were predictive of graduate performance. These findings question the value of measurable application data in resident selection and highlight the importance of cultivating outstanding surgeons throughout training.