Use of a Machine Learning Program for Urogynecology Fellowship Applicant Review
A significant positive correlation was found between Halsted rankings of applicants and rankings assigned by PDs. In addition, significant differences in interprogram rankings of applicants suggest that there is a range of qualities that each program values and that application review is individualized. Machine learning assistance in application review is a developing tool with the potential to reduce bias and decrease program administrative burden.
Authors
- Christine M. Vaccaro (ORCID: https://orcid.org/0000-0001-7112-667X)
- Nicole Wood (ORCID: https://orcid.org/0000-0002-7438-759X)
- Elena Tunitsky-Bitton (ORCID: https://orcid.org/0000-0002-9378-0979)
- Leslie Rickey (ORCID: https://orcid.org/0000-0002-1306-8982)
- Rui Wang
Institutions
- Hartford Hospital (US)
- Grand View University (US)
- Walter Reed National Military Medical Center (US)
- Yale University (US)
Publication Details
- Journal
- Urogynecology
- Published
- 2026-02-02
- DOI
- https://doi.org/10.1097/spv.0000000000001798
- Primary Topic
- Diversity and Career in Medicine
- Type
- article
- Field-Weighted Citation Impact
- 0.00