March 2026
Plastic surgery residency applicants face a high-stakes, emotionally charged decision-how to rank the programs at which they have interviewed.
Why this matters
Plastic surgery residency applicants face a high-stakes, emotionally charged decision-how to rank the programs at which they have interviewed. This work contributes to evidence-based plastic and reconstructive surgery — helping clinicians interpret outcomes, refine technique, and counsel patients with clearer data.
Author
Plastic and reconstructive surgery resident at Emory University (UCLA BS, USC MD) with an h-index of 15 and peer-reviewed work spanning aesthetic surgery, reconstruction, medical devices, and AI in medicine.
Abstract
Plastic surgery residency applicants face a high-stakes, emotionally charged decision-how to rank the programs at which they have interviewed. Although programs evaluate applicants using structured interview processes, application metrics, and faculty consensus, applicants' program assessments are often subjective, inconsistent, and influenced by emotion, perceived (and sometimes misplaced) prestige, or recency bias. Despite the personal and professional consequences of an ill-informed rank list, there is no standardized framework to help applicants objectively compare programs. We propose the Residency Applicant Numeric Key (RANK) tool to standardize program evaluation for plastic surgery residency applicants. RANK uses a head-to-head scoring approach that prompts applicants to rate each program across 10 domains, including case volume, surgical autonomy, county and community hospital exposure, case diversity, program culture, global surgery, research, location, and other applicant-specific factors. Each domain is scored on a 1-5 scale. Domain scores are summed to generate an overall score, enabling direct numerical comparison and organized ranking across programs. The RANK tool provides a consistent structure to support objective, side-by-side evaluation while preserving room for individualized priorities through domain selection and weighting.