May 10, 2023

1:00 pm / 3:00 pm

TITLE: Career Trajectory of Academic General Surgery Residency Program Graduates: Do Academic Programs Graduate Academic Surgeons?


Allen Green (1)

Jeff Choi (1)

(1) Department of Surgery

DATE: Wednesday, 10 May 2023

TIME: 1:30–3:00 PM

LOCATION: Conference Room X399, Medical School Office Building, 1265 Welch Road, Stanford, CA


The Data Studio Workshop brings together a biomedical investigator with a group of experts for an in-depth session to solicit advice about statistical and study design issues that arise while planning or conducting a research project. This week, the investigator(s) will discuss the following project with the group.


Many general surgery residency programs emphasize their ability to produce academic surgeons. However, the proportion of academic general surgery residency graduates who become academic surgeons remains unclear.


We aimed to quantify the contemporary prevalence of US academic general surgery residency graduates who become academic surgeons, and elucidate factors associated with pursuing a career in academic surgery.


We identified 2015 and 2018 graduates from 97 Accreditation Council for Graduate Medical Education-accredited general surgery residency programs affiliated with US allopathic medical schools. We extracted program and individual-level data using publicly available Doximity, PubMed, residency program, and faculty profiles. We defined academic surgeons as faculty within university-affiliated surgery departments who published two or more papers as the first or senior author in 2020 and 2021. Using a stepwise likelihood ratio test method to identify covariates, a multivariable logistic regression evaluated associations between program and individual-level factors and a career in academic surgery. The threshold for statistical significance was P <0.05.


We hope to build a logistic regression model that provides us inferences on what residency program-level and resident-level factors are associated with pursuing a career in academic surgery. We plan to use this model to first compare between non-academic and academic surgeons. Secondly, we plan to repeat this analysis comparing a highly productive subset of academic surgeons and the rest of the academic surgeon cohort to identify factors associated with being a highly productive academic surgeon.


(1) Given the complex relationships between our variables, what is the best way to select variables to minimize confounders?

(2) Are there any additional analysis methods outside of logistic regression that we should consider?


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