Predictors of future academic productivity in pediatric otolaryngology fellowship applicants.

OBJECTIVES: To investigate which components of pediatric otolaryngology fellowship applications are more closely predictive of future academic productivity in applicants who go on to complete their fellowship training. METHODS: Applications to our institution's ACGME accredited pediatric otolaryngology fellowship program through the SF Match program for the years 2011-2016 were reviewed. Applicant files on record were utilized to extract independent variables including sex, mean USMLE score, residency program Doximity ranking, military experience, number of national honors/awards, AOA status, total number of publications listed on application, number of first author publications listed on application, and AAOHNS Committee involvement. Academic productivity was determined by number of PubMed indexed publications per year, practice setting, and H-index (Scopus). Statistical analysis consisted of multivariate and univariate regression models, with p < 0.05 being considered statistically significant. RESULTS: Multivariate regression showed that USMLE Step 1 and 2 mean score and number of publications listed on application exhibited statistically significant correlations with a higher number of future post fellowship publications per year. Residency program Doximity rank, applicant number of awards and honors, AOA status, and number of first author publications were not predictive of future academic productivity. No statistically significant associations were found between any variables and the faculty position outcome variable. CONCLUSIONS: Quantifiable criteria in pediatric otolaryngology fellowship applications, such as number of listed publications and mean USMLE scores are strongly correlated with future academic productivity metrics.

Deep Learning for Predictive Analysis of Pediatric Otolaryngology Personal Statements: A Pilot Study.

OBJECTIVE: The personal statement is often an underutilized aspect of pediatric otolaryngology fellowship applications. In this pilot study, we use deep learning language models to cluster personal statements and elucidate their relationship to applicant rank position and postfellowship research output. STUDY DESIGN: Retrospective cohort. SETTING: Single pediatric tertiary care center. METHODS: Data and personal statements from 115 applicants to our fellowship program were retrieved from San Francisco Match. BERT (Bidirectional Encoder Representations From Transformers) was used to generate document embeddings for clustering. Regression and machine learning models were used to assess the relationship of personal statements to number of postfellowship publications per year when controlling for publications, board scores, Alpha Omega Alpha status, gender, and residency. RESULTS: Document embeddings of personal statements were found to cluster into 4 distinct groups by K-means clustering: 2 focused on "training/research" and 2 on "personal/patient anecdotes." Training clusters 1 and 2 were associated with an applicant-organization fit by a single pediatric otolaryngology fellowship program on univariate but not multivariate analysis. Models utilizing document embeddings alone were able to equally predict applicant-organization fit (receiver operating characteristic areas under the curve, 0.763 and 0.750 vs 0.419; P values >.05) as compared with models utilizing applicant characteristics and personal statement clusters alone. All predictive models were poor predictors of postfellowship publications per year. CONCLUSION: We demonstrate ability for document embeddings to capture meaningful information in personal statements from pediatric otolaryngology fellowship applicants. A larger study can further differentiate personal statement clusters and assess the predictive potential of document embeddings.