Validation of Orthopedic Surgery Residency Database for Use Within Plastic Surgery

Area of Science:

• Medical Education
• Bibliometrics
• Plastic Surgery Research

Background:

• Matched plastic surgery (PS) residency applicants demonstrate higher publication rates than unmatched peers.
• Current residency matching data aggregates research metrics, limiting program-specific insights.
• Previous orthopedic surgery research utilized Python and Scopus API for data collection.

Purpose of the Study:

• To develop a comprehensive, validated database of plastic surgery resident research output and demographics.
• To build upon existing models for automated data collection in medical residency research.
• To provide data-driven insights for plastic surgery residency applicants.

Main Methods:

• Identified 88 active integrated PS residency programs and 1170 residents.
• Collected resident demographics (degree, sex, medical school) via program websites, social media, and the Residency Explorer (RE) tool.
• Employed Python scripts interfaced with the Scopus API for bibliometric data extraction, followed by manual verification.

Main Results:

• Successfully identified and verified 14,385 publications attributed to 1055 residents from an initial script output of 38,226.
• The methodology achieved 90% resident capture rate across multiple queries.
• Demographic data matching with RE showed 80% for degree type and 62% for gender.

Conclusions:

• Automated data mining of public sources can construct a robust, validated database of PS research output.
• This methodology is scalable to other medical specialties.
• The developed database can inform predictive tools for residency applicant guidance and program selection.