TY - JOUR
T1 - Predicting General Surgery Match Outcomes Using Standardized Ranking Metrics
AU - Mao, Rui Min D.
AU - Williams, Taylor P.
AU - Price, Anthony
AU - Colvill, Kayla M.
AU - Cummins, Claire B.
AU - Radhakrishnan, Ravi S.
N1 - Publisher Copyright:
© 2022 Elsevier Inc.
PY - 2023/3
Y1 - 2023/3
N2 - Introduction: Objective measurements for applicant ranking are becoming increasingly important, not only to help address the growing number of general surgery applicants each year but also to minimize bias and ensure consistency. We assessed if our general surgery applicant scoring system was an effective tool for accurately predicting the results of the resident match. Methods: A retrospective review of applicant rank lists from 2017 to 2020 was conducted. Applicants were ranked based on the sum of preinterview and interview scores. The preinterview score is an objective metric related to the applicant's academic portfolio. The interview score is a standardized score based on interview performance. We reviewed match results from ranked candidates and categorized them as academic categorical (AC), community categorical (CC), preliminary surgical (PS), nonsurgical specialty (NS), or unmatched (UM) positions. Results: A total of 378 applicants were interviewed. Forty-nine percent matched into AC, 22% into CC, 11% into PS, and 5% into NS positions, while 13% of the interviewees were UM. Applicants who matched into AC positions had significantly higher preinterview and interview scores than applicants in other categories. Applicants who matched into CC positions had significantly higher interview scores than those categorized as UM, but their preinterview scores did not differ significantly from the UM group. Applicants who did not match into a categorical position (PS, NS, or UM) did not have significantly different preinterview or interview scores from one another. Conclusions: Our standardized scoring system was effective in stratifying which applicants would match into categorical general surgery residency programs.
AB - Introduction: Objective measurements for applicant ranking are becoming increasingly important, not only to help address the growing number of general surgery applicants each year but also to minimize bias and ensure consistency. We assessed if our general surgery applicant scoring system was an effective tool for accurately predicting the results of the resident match. Methods: A retrospective review of applicant rank lists from 2017 to 2020 was conducted. Applicants were ranked based on the sum of preinterview and interview scores. The preinterview score is an objective metric related to the applicant's academic portfolio. The interview score is a standardized score based on interview performance. We reviewed match results from ranked candidates and categorized them as academic categorical (AC), community categorical (CC), preliminary surgical (PS), nonsurgical specialty (NS), or unmatched (UM) positions. Results: A total of 378 applicants were interviewed. Forty-nine percent matched into AC, 22% into CC, 11% into PS, and 5% into NS positions, while 13% of the interviewees were UM. Applicants who matched into AC positions had significantly higher preinterview and interview scores than applicants in other categories. Applicants who matched into CC positions had significantly higher interview scores than those categorized as UM, but their preinterview scores did not differ significantly from the UM group. Applicants who did not match into a categorical position (PS, NS, or UM) did not have significantly different preinterview or interview scores from one another. Conclusions: Our standardized scoring system was effective in stratifying which applicants would match into categorical general surgery residency programs.
KW - General surgery
KW - Match
KW - Residency application
KW - Resident ranking
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U2 - 10.1016/j.jss.2022.11.038
DO - 10.1016/j.jss.2022.11.038
M3 - Article
C2 - 36915008
AN - SCOPUS:85143791256
SN - 0022-4804
VL - 283
SP - 817
EP - 823
JO - Journal of Surgical Research
JF - Journal of Surgical Research
ER -