title: Nomogram to predict successful placement in surgical subspecialty fellowships using applicant characteristics.
abstract: OBJECTIVE
  The purpose of the study was to develop a model that predicts an individual applicant's probability of successful placement into a surgical subspecialty fellowship program.
  
  
  METHODS
  Candidates who applied to surgical fellowships during a 3-year period were identified in a set of databases that included the electronic application materials.
  
  
  RESULTS
  Of the 1281 applicants who were available for analysis, 951 applicants (74%) successfully placed into a colon and rectal surgery, thoracic surgery, vascular surgery, or pediatric surgery fellowship. The optimal final prediction model, which was based on a logistic regression, included 14 variables. This model, with a c statistic of 0.74, allowed for the determination of a useful estimate of the probability of placement for an individual candidate.
  
  
  CONCLUSIONS
  Of the factors that are available at the time of fellowship application, 14 were used to predict accurately the proportion of applicants who will successfully gain a fellowship position.
authors.affiliation: Center for Urogynecology and Pelvic Reconstructive Surgery, Obstetrics, Gynecology and Women's Health Institute, Cleveland Clinic, Cleveland, Ohio 44195, USA. mufflyt@ccf.org
authors.forename: Tyler M
authors.initials: TM
authors.lastname: Muffly
authors.forename: Matthew D
authors.initials: MD
authors.lastname: Barber
authors.forename: Matthew T
authors.initials: MT
authors.lastname: Karafa
authors.forename: Michael W
authors.initials: MW
authors.lastname: Kattan
authors.forename: Abigail
authors.initials: A
authors.lastname: Shniter
authors.forename: J Eric
authors.initials: JE
authors.lastname: Jelovsek
mesh_terms: D000328:Adult; D002321:Career Choice; D016208:Databases, Factual; D005257:Fellowships and Scholarships / Q000592:standards* / Q000639:trends; D005260:Female; D006801:Humans; D007586:Job Application; D008297:Male; D049451:Nomograms; D010555:Personality Inventory; D010560:Personnel Selection; D011237:Predictive Value of Tests; D013043:Specialties, Surgical / Q000193:education*; D014481:United States