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Trends in Cornea Fellowship Applications and Applicant Characteristics: A San Francisco Match Analysis.

Brittany C TsouUgochi T AguwaLubaina T ArsiwalaEleanor BurtonKapil MishraSidra ZafarFasika A Woreta
Published in: Journal of academic ophthalmology (2017) (2022)
Purpose  We investigate trends in cornea fellowship positions filled over time and applicant characteristics associated with matching into cornea fellowship. Methods  Characteristics of cornea fellowship applicants were assessed using deidentified 2010 to 2017 San Francisco (SF) Match data. Publicly available SF Match cornea fellowship data including the number of participating programs, number of positions offered, number of positions filled, percentage of positions filled, and number of vacancies from 2014 to 2019 were also analyzed as data from 2010 to 2013 were unavailable. Results  From 2014 to 2019, the number of cornea fellowship programs increased by 11.3% (mean 2.3% per year, p  = 0.006) and the number of positions offered increased by 7.7% (mean 1.4% per year, p  = 0.065). Of 1,390 applicants from 2010 to 2017, 589 (42.4%) matched into cornea. After controlling for potential covariates, graduation from a U.S residency program (odds ratio [OR]: 6.15, 95% confidence interval [CI]: 4.05-9.35, p  < 0.001) and a greater number of interviews completed (OR: 1.35, 95% CI: 1.29-1.42, p  < 0.001) were associated with increased odds of cornea fellowship match. A greater number of applied programs (OR: 0.97, 95% CI: 0.95-0.98, p  < 0.001) was associated with decreased odds of matching into cornea fellowship. The proportion of applicants matching into cornea fellowship increased until 30 applications. Conclusions  The number of cornea fellowship programs and positions increased from 2014 to 2019. Graduation from a U.S residency program and a greater number of interviews completed were associated with an increased likelihood of cornea fellowship match. Unlike applying to any ophthalmology subspecialty fellowship, applying to greater than 30 cornea fellowship programs was associated with decreased odds of matching.
Keyphrases
  • public health
  • electronic health record
  • quality improvement
  • machine learning
  • big data
  • deep learning
  • medical students