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Validation of Orthopedic Surgery Residency Database for Use Within Plastic Surgery.

Isabella Zorra¹, Tyler Miller¹, Allison Raymundo¹

¹From the Division of Plastic, Reconstructive & Cosmetic Surgery, University of Illinois College of Medicine at Chicago, Chicago, IL.

Plastic and Reconstructive Surgery. Global Open

|July 15, 2026

Summary

Plastic surgery residency applicants with higher publication counts are more likely to match. This study created a comprehensive database of plastic surgery resident research output, aiding future applicant decisions.

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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.