Predictive Value of Preresidency Academic Metrics on Resident Publication Potential

Abstract

Objective Otolaryngology residency is highly competitive, and applicant academic metrics are scrutinized. The predictive value of preresidency academic metrics on applicants’ future research productivity and career aspirations remains largely undefined. Study Design Retrospective cohort study Setting Academic otolaryngology department, 2014 to 2015. Methods Applicant demographics, publication history, and United States Medical Licensing Examination (USMLE) scores were downloaded from Electronic Residency Application Service archives. Publications during residency were tallied from all PubMed articles indexed between July 1, 2015 and June 30, 2020. Postresidency career paths were examined by 2 investigators (D.J.C. and L.X.Y.) using Google searches with an emphasis on program websites, Doximity, and LinkedIn profiles. Associations with publication potential and postresidency positions were evaluated with Spearman rank correlation coefficients and Kruskal‐Wallis, Wilcoxon rank sum, and χ 2 tests. Results Of 321 applicants, 226 (70%) matched, and 205 (64%) completed residency by June 2020. Matched residents published a median of 4 (range: 0‐41) manuscripts during residency. USMLE scores, Alpha Omega Alpha status, and the number of preresidency publications did not significantly correlate with publication potential during residency. The number of research experiences had a significant positive correlation with publications during residency ( p < 0.001). Asian race ( p = 0.002) and geographical region of residency ( p < 0.001) also had significant associations with publication potential. Of the 205 graduates, 118 (58%) enrolled in fellowship. Age and female sex (74% vs 48%; p = 0.002) were the only factors significantly associated with pursuing a fellowship. Conclusion In otolaryngology, not all preresidency academic metrics are associated with publication potential during residency or propensity for fellowship training. Programs should not use academic metrics alone to predict an applicant's future research productivity or career trajectory.