Background: Plastic surgery remains one of the most competitive medical specialties, with match rates varying significantly across applicant groups. Understanding predictive factors for successful residency matching is crucial for medical students and program directors. This study develops a comprehensive analytical pipeline to identify key predictors of plastic surgery match outcomes using official NRMP data from 2009-2025. Methods: Python pandas were used to automate data acquisition and analysis was produced, which downloads official NRMP reports and extracts structured data on applicant characteristics and match outcomes. Analysis included 9,547 applicant records across four major groups: US MD seniors, DO seniors, US IMGs, and Non-US IMGs. Three complementary machine learning models (Logistic Regression, Random Forest, XGBoost) with stratified cross-validation and SMOTE oversampling for class imbalance. Comprehensive statistical analysis included multivariate logistic regression, ANOVA tests for continuous variables, chi-square tests for categorical variables, and odds ratio calculations with 95% confidence intervals. Results: The overall plastic surgery match rate was 43.9%, with significant disparities across applicant groups (p<0.001). US MD seniors achieved the highest match rate (71.8%), followed by DO seniors (42.0%), US IMGs (32.3%), and Non-US IMGs (20.4%). Research experiences emerged as the most significant predictor (F=420, p<0.001), followed by publications/abstracts (F=1341, p<0.001). USMLE Step 1 and Step 2 CK scores showed significant group differences (p<0.001). AOA membership was positively associated with matching for US MD seniors (OR=1.27, p=0.022) but negatively for Non-US IMGs (OR=0.71, p=0.008). All 10 pairwise group comparisons revealed statistically significant differences in match rates. Conclusions: This analysis provides robust statistical evidence for substantial disparities in plastic surgery match outcomes across applicant groups. Research productivity and USMLE performance are key modifiable factors that significantly influence match success. The automated pipeline offers a scalable framework for ongoing residency match analysis and can inform evidence-based advising strategies for medical students pursuing competitive specialties.
Daneshi et al. (Mon,) studied this question.