title: What characteristics of applicants to emergency medicine residency programs predict future success as an emergency medicine resident?
abstract: OBJECTIVE
  Program directors of emergency medicine (EM) residencies attempt to select candidates who will subsequently perform well as residents. This study was undertaken to identify characteristics available at the time of application to an EM residency that predict future success in residency.
  
  
  METHODS
  The EM faculty at the University of California San Diego (UCSD) completed a one-time confidential assessment of EM residents on performance in residency at the time of graduation. The faculty member compared the graduate with all residents (both EM and non-EM) with whom the faculty member had previously worked using the five-point scale: > or =90th percentile, 70th-89th percentile, 50th-69th percentile, 30th-49th percentile, or < 30th percentile. Descriptive statistics, ordinal logistic regression (OLR), classification and regression tree (CART) analysis, and multiple additive regression tree (MART) analysis were used to find predictors for each of the outcome variables.
  
  
  RESULTS
  Fifty-four graduates were evaluated. The medical school attended (MSA) was the strongest predictor of overall performance in residency in all regression models. OLR showed that MSA and "distinctive factors" (being a championship athlete, medical school officer, etc.) were significant predictors and may deserve greater weighting in the selection process. The most robust MART model demonstrated that MSA, dean's letter of recommendation, and distinctive factors had the most impact on overall performance in an EM residency.
  
  
  CONCLUSIONS
  Using regression modeling, it may be possible to predict future resident performance from characteristics contained in residency applications. Applicants from top-tier medical schools and those with distinctive talents were more successful in the UCSD EM residency.
authors.affiliation: University of California-San Diego, 200 West Arbor Drive, Mailcode 8676, San Diego, CA 92103, USA. shayden@ucsd.edu
authors.forename: Stephen R
authors.initials: SR
authors.lastname: Hayden
authors.forename: Marina
authors.initials: M
authors.lastname: Hayden
authors.forename: Anthony
authors.initials: A
authors.lastname: Gamst
mesh_terms: D000328:Adult; D002140:California; D002983:Clinical Competence / Q000706:statistics & numerical data; D004521:Educational Measurement / Q000706:statistics & numerical data; D004635:Emergency Medicine / Q000193:education* / Q000639:trends; D005260:Female; D005544:Forecasting; D006801:Humans; D007396:Internship and Residency / Q000706:statistics & numerical data* / Q000639:trends; D016015:Logistic Models; D008297:Male; D010560:Personnel Selection / Q000706:statistics & numerical data* / Q000639:trends; D010865:Pilot Projects; D012044:Regression Analysis; D012189:Retrospective Studies