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Abstract

Objective To develop a probability model of matching into a US ophthalmology residency program using San Francisco Matching Program (SF Match) data.

Design Retrospective data analysis of de-identified application and matching data.

Participants Registrants for the 2013, 2014, and 2015 ophthalmology residency matches conducted by the SF Match.

Methods Descriptive statistics of candidates, comparison of continuous and categorical variables between matched and nonmatched candidates, and linear regression modeling were performed. A recursive partitioning method was used to create a probability of matching algorithm.

Main Outcome Measures Probability of successfully matching based on quantifiable candidate characteristics.

Results Over the 3-year period, 1,959 individuals submitted an average of 64 applications and received a mean of nine interview invitations. The overall match rate was 71%, with 78% matching at one of their top five choices. Successful matches were more likely to occur in US medical school graduates (78% vs 20%, p < 0.001) and applicants on their first attempt (76% vs 29%, p < 0.001). The association between matching and number of programs applied became negative with > 48 applications. Probability of matching was “high” (> 80%) among US graduates with a step 1 United States Medical Licensing Examination (USMLE) score >243 (regardless of number of programs applied to), a step 1 USMLE score of 231 to 243 who applied to at least 30 programs, and first-time applicants with a step 1 score >232. No international medical graduates or repeat applicants had a “high” probability of matching.

Conclusions Although advice must be individualized for each candidate, applicants for ophthalmology residency who fall into a “high” probability of matching group are likely to be successful with applications to 45 or fewer programs. Applying to 80 or more programs should be considered for international medical graduates and/or applicants who are previously unmatched. Modification of the match application data form may allow more detailed analysis of variables such as Alpha Omega Alpha or Gold Humanism Honor Society membership, research activity, and composite evaluation on a standardized letter of recommendation.

Online Available Date

October 29, 2018

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