Plastic and reconstructive surgery. Global open · 2026
Validation of Orthopedic Surgery Residency Database for Use Within Plastic Surgery.
СтатьяНазвание и аннотация: Перевод в очереди
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Zorra Isabella, Miller Tyler, Raymundo Allison, Xu Catherine, Smith Benjamin, Jacome Freddy P, Lawery Quinn, Langone Christina, Corcoran Julia F
Аннотация
BACKGROUND: Matched plastic surgery (PS) residency applicants report higher mean numbers of abstracts, presentations, and publications than unmatched peers. However, the National Resident Matching Program aggregates these metrics, limiting insight into program-specific trends. Building on an orthopedic surgery model that uses Python scripts interfaced with the Elsevier Scopus Application Programming Interface, this study aims to create a comprehensive database of PS resident research output and demographics.
METHODS: Eighty-eight active integrated PS residency programs were identified via the Accreditation Council for Graduate Medical Education. A total of 1170 residents (postgraduate years 1-6) were identified through official program websites and social media and validated with the National Resident Matching Program data. Resident demographics (degree type, sex, medical school) were verified using the Residency Explorer (RE) tool. A Python script was used to collect bibliometric data, including publication count, authorship role, journal name, impact factor, and citation count. Manual verification was followed to confirm authorship.
RESULTS: The script identified 38,226 publications, of which 14,385 were verified and attributed to 1055 residents. This methodology captured 90% of residents within multiple queries. Degree type data matched RE data in 80% of programs, and gender data matched RE data in 62% of programs.
CONCLUSIONS: This study demonstrates the feasibility of using automated data mining and public sources to construct a robust, validated database of PS research output. The methodology offers scalability to other specialties and lays the groundwork for predictive tools to support applicants in selecting programs that align with their academic profiles, as well as guiding away-rotation and signaling decisions.
METHODS: Eighty-eight active integrated PS residency programs were identified via the Accreditation Council for Graduate Medical Education. A total of 1170 residents (postgraduate years 1-6) were identified through official program websites and social media and validated with the National Resident Matching Program data. Resident demographics (degree type, sex, medical school) were verified using the Residency Explorer (RE) tool. A Python script was used to collect bibliometric data, including publication count, authorship role, journal name, impact factor, and citation count. Manual verification was followed to confirm authorship.
RESULTS: The script identified 38,226 publications, of which 14,385 were verified and attributed to 1055 residents. This methodology captured 90% of residents within multiple queries. Degree type data matched RE data in 80% of programs, and gender data matched RE data in 62% of programs.
CONCLUSIONS: This study demonstrates the feasibility of using automated data mining and public sources to construct a robust, validated database of PS research output. The methodology offers scalability to other specialties and lays the groundwork for predictive tools to support applicants in selecting programs that align with their academic profiles, as well as guiding away-rotation and signaling decisions.