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Maxfield CM, Montano-Campos JF, Gould J, Koontz NA, Milburn J, Omofoye T, Peterson R, Seekins J, Grimm L. The Influence of Extracurricular Activities on Radiology Resident Selection Decisions. J Am Coll Radiol 2023:S1546-1440(23)00844-X. [PMID: 37922965 DOI: 10.1016/j.jacr.2023.09.013] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 07/09/2023] [Revised: 09/14/2023] [Accepted: 09/20/2023] [Indexed: 11/07/2023]
Abstract
PURPOSE Extracurricular activities (EAs) listed on radiology residency applications can signal traits and characteristics desired in holistic reviews. The authors conducted an objective analysis to determine the influence of EAs on resident selection decisions. METHODS A discrete-choice experiment was designed to model radiology resident selection and determine the relative weights of EAs among academic and demographic application factors. Faculty members involved in resident selection at 30 US radiology programs chose between hypothetical pairs of applicant profiles between October 2021 and February 2022. Each applicant profile included one of 22 EAs chosen for study. A conditional logistic regression model assessed the relative weights of the attributes and odds ratios (ORs) were calculated. RESULTS Two hundred forty-four participants completed the exercise. Community-service EAs were ranked most highly by participants. LGBTQ Pride Alliance (OR, 1.56; 95% confidence interval [CI], 1.14-2.15; P = .006) and Young Republicans (OR, 0.60; 95% CI, 0.43-0.82; P = .001) significantly influenced decisions. The highest ranked EAs were significantly preferred over the lowest ranked EAs (OR, 1.916; 95% CI, 1.671-2.197; P < .001). Participants preferred EAs that reflected active over passive engagement (OR, 1.154; 95% CI, 1.022-1.304; P = .021) and progressive over conservative ideology (OR, 1.280; 95% CI, 1.133-1.447; P < .001). Participants who ranked progressive EAs more highly preferred applicants with progressive EAs (P < .05 for all). CONCLUSIONS The influence of EAs on resident selection decisions is significant and likely to gain importance in resident selection as medical student performance metrics are further eliminated. Applicants and selection committees should consider this influence and the bias that EAs can bring to resident selection decisions.
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Affiliation(s)
- Charles M Maxfield
- Vice Chair of Education, Department of Radiology, Duke University Medical Center, Durham, North Carolina.
| | | | - Jennifer Gould
- Program Director, Diagnostic Radiology Residency, Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, Missouri
| | - Nicholas A Koontz
- Director of Fellowship Programs, Dean D. T. Maglinte Scholar in Radiology Education, Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana
| | - James Milburn
- Vice Chairman of Radiology, Residency Program Director, Department of Radiology, Ochsner Health System, New Orleans, Louisiana
| | - Toma Omofoye
- Strategic Director of Education, Department of Breast Imaging, The University of Texas MD Anderson Cancer Center, Houston, Texas. https://twitter.com/TomaOmofoyeMD
| | - Ryan Peterson
- Program Director for Diagnostic Radiology Residency, Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia. https://twitter.com/RyanBPetersonMD
| | - Jayne Seekins
- Associate Program Director, Diagnostic Radiology Residency and Pediatric Radiology Fellowship, Department of Radiology, Stanford University, Stanford, California
| | - Lars Grimm
- Department of Radiology, Duke University Medical Center, Durham, North Carolina. https://twitter.com/Dr_Lars_Grimm
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Montano-Campos JF, Stout JE, Pettit AC, Okeke NL. Association of Neighborhood Deprivation With Healthcare Utilization Among Persons With Human Immunodeficiency Virus: A Latent Class Analysis. Open Forum Infect Dis 2023; 10:ofad317. [PMID: 37426949 PMCID: PMC10326676 DOI: 10.1093/ofid/ofad317] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/17/2023] [Accepted: 06/11/2023] [Indexed: 07/11/2023] Open
Abstract
Background We previously identified 3 latent classes of healthcare utilization among people with human immunodeficiency virus (PWH): adherent, nonadherent, and sick. Although membership in the "nonadherent" group was associated with subsequent disengagement from human immunodeficiency virus (HIV) care, socioeconomic predictors of class membership remain unexplored. Methods We validated our healthcare utilization-based latent class model of PWH receiving care at Duke University (Durham, North Carolina) using patient-level data from 2015 to 2018. SDI scores were assigned to cohort members based on residential addresses. Associations of patient-level covariates with class membership were estimated using multivariable logistic regression and movement between classes was estimated using latent transition analysis. Results A total of 1443 unique patients (median age of 50 years, 28% female sex at birth, 57% Black) were included in the analysis. PWH in the most disadvantaged (highest) SDI decile were more likely to be in the "nonadherent" class than the remainder of the cohort (odds ratio [OR], 1.58 [95% confidence interval {CI}, .95-2.63]) and were significantly more likely to be in the "sick" class (OR, 2.65 [95% CI, 2.13-3.30]). PWH in the highest SDI decile were also more likely to transition into and less likely to transition out of the "sick" class. Conclusions PWH who resided in neighborhoods with high levels of social deprivation were more likely to have latent class membership in suboptimal healthcare utilization groupings, and membership persisted over time. Risk stratification models based on healthcare utilization may be useful tools in the early identification of persons at risk for suboptimal HIV care engagement.
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Affiliation(s)
- J Felipe Montano-Campos
- Comparative Health Outcomes, Policy, and Economics (CHOICE) Institute, School of Pharmacy, University of Washington, Seattle, Washington, USA
| | - Jason E Stout
- Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina, USA
| | - April C Pettit
- Division of Infectious Diseases, Vanderbilt University Medical Center, Nashville, Tennessee, USA
| | - Nwora Lance Okeke
- Division of Infectious Diseases, Duke University Medical Center, Durham, North Carolina, USA
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Montano-Campos JF, Gonzalez JM, Rickert T, Fairchild AO, Levitan B, Reed SD. Use of Patient Preferences Data Regarding Multiple Risks to Inform Regulatory Decisions. MDM Policy Pract 2023; 8:23814683221148715. [PMID: 36654678 PMCID: PMC9841858 DOI: 10.1177/23814683221148715] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/20/2021] [Accepted: 12/07/2022] [Indexed: 01/15/2023] Open
Abstract
Background and Objectives. Risk-tolerance measures from patient-preference studies typically focus on individual adverse events. We recently introduced an approach that extends maximum acceptable risk (MAR) calculations to simultaneous maximum acceptable risk thresholds (SMART) for multiple treatment-related risks. We extend these methods to include the computation and display of confidence intervals and apply the approach to 3 published discrete-choice experiments to evaluate its utility to inform regulatory decisions. Methods. We generate MAR estimates and SMART curves and compare them with trial-based benefit-risk profiles of select treatments for depression, psoriasis, and thyroid cancer. Results. In the depression study, SMART curves with 70% to 95% confidence intervals portray which combinations of 2 adverse events would be considered acceptable. In the psoriasis example, the asymmetric confidence intervals for the SMART curve indicate that relying on independent MARs versus SMART curves when there are nonlinear preferences can lead to decisions that could expose patients to greater risks than they would accept. The thyroid cancer application shows an example in which the clinical incidence of each of 3 adverse events is lower than the single-event MARs for the expected treatment benefit, yet the collective risk profile surpasses acceptable levels when considered jointly. Limitations. Nonrandom sample of studies. Conclusions. When evaluating conventional MARs in which the observed incidences are near the estimated MARs or where preferences demonstrate diminishing marginal disutility of risk, conventional MAR estimates will overstate risk acceptance, which could lead to misinformed decisions, potentially placing patients at greater risk of adverse events than they would accept. Implications. The SMART method, herein extended to include confidence intervals, provides a reproducible, transparent evidence-based approach to enable decision makers to use data from discrete-choice experiments to account for multiple adverse events. Highlights Estimates of maximum acceptable risk (MAR) for a defined treatment benefit can be useful to inform regulatory decisions; however, the conventional metric considers one adverse event at a time.This article applies a new approach known as SMART (simultaneous maximum acceptable risk thresholds) that accounts for multiple adverse events to 3 published discrete-choice experiments.Findings reveal that conventional MARs could lead decision makers to accept a treatment based on individual risks that would not be acceptable if multiple risks are considered simultaneously.
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Affiliation(s)
| | - Juan Marcos Gonzalez
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA,Department of Population Health Sciences, Duke University School of Medicine, Durham, NC, USA
| | - Timothy Rickert
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | - Angelyn O. Fairchild
- Duke Clinical Research Institute, Duke University School of Medicine, Durham, NC, USA
| | | | - Shelby D. Reed
- Shelby D. Reed, Duke Clinical Research Institute, Duke University School of Medicine, 300 W Morgan Street, Durham, NC 27701, USA; ()
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Maxfield CM, Montano-Campos JF, Chapman T, Desser TS, Ho CP, Hull NC, Kelly HR, Kennedy TA, Koontz NA, Knippa EE, McLoud TC, Milburn J, Mills MK, Morgan DE, Morgan R, Peterson RB, Salastekar N, Thorpe MP, Zarzour JG, Reed SD, Grimm LJ. Factors Influential in the Selection of Radiology Residents in the Post-Step 1 World: A Discrete Choice Experiment. J Am Coll Radiol 2021; 18:1572-1580. [PMID: 34332914 DOI: 10.1016/j.jacr.2021.07.005] [Citation(s) in RCA: 9] [Impact Index Per Article: 3.0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/20/2021] [Revised: 07/06/2021] [Accepted: 07/09/2021] [Indexed: 11/30/2022]
Abstract
OBJECTIVES Reporting of United States Medical Licensing Examination Step 1 results will transition from a numerical score to a pass or fail result. We sought an objective analysis to determine changes in the relative importance of resident application attributes when numerical Step 1 results are replaced. METHODS A discrete choice experiment was designed to model radiology resident selection and determine the relative weights of various application factors when paired with a numerical or pass or fail Step 1 result. Faculty involved in resident selection at 14 US radiology programs chose between hypothetical pairs of applicant profiles between August and November 2020. A conditional logistic regression model assessed the relative weights of the attributes, and odds ratios (ORs) were calculated. RESULTS There were 212 participants. When a numerical Step 1 score was provided, the most influential attributes were medical school (OR: 2.35, 95% confidence interval [CI]: 2.07-2.67), Black or Hispanic race or ethnicity (OR: 2.04, 95% CI: 1.79-2.38), and Step 1 score (OR: 1.8, 95% CI: 1.69-1.95). When Step 1 was reported as pass, the applicant's medical school grew in influence (OR: 2.78, 95% CI: 2.42-3.18), and there was a significant increase in influence of Step 2 scores (OR: 1.31, 95% CI: 1.23-1.40 versus OR 1.57, 95% CI: 1.46-1.69). There was little change in the relative influence of race or ethnicity, gender, class rank, or clerkship honors. DISCUSSION When Step 1 reporting transitions to pass or fail, medical school prestige gains outsized influence and Step 2 scores partly fill the gap left by Step 1 examination as a single metric of decisive importance in application decisions.
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Affiliation(s)
- Charles M Maxfield
- Vice-Chair of Education, Department of Radiology, Duke University Medical Center, Durham, North Carolina.
| | - J Felipe Montano-Campos
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Teresa Chapman
- Residency Program Director, Department of Radiology, Seattle Children's Hospital, University of Washington School of Medicine, Seattle, Washington
| | - Terry S Desser
- Department of Radiology, Stanford University Medical Center, Stanford, California
| | - Christopher P Ho
- Residency Program Director, Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Nathan C Hull
- Department of Radiology, Mayo Clinic, Rochester, Minnesota
| | - Hillary R Kelly
- Department of Radiology, Massachusetts General Hospital, Boston, Massachusetts; Massachusetts Eye and Ear, Harvard Medical School, Boston, Massachusetts
| | - Tabassum A Kennedy
- Department of Radiology, University of Wisconsin School of Medicine and Public Health, Madison, Wisconsin
| | - Nicholas A Koontz
- Department of Radiology and Imaging Sciences, Indiana University School of Medicine, Indianapolis, Indiana
| | - Emily E Knippa
- Department of Radiology, University of Texas Southwestern Medical Center, Dallas, Texas
| | - Theresa C McLoud
- Vice-Chair of Education, Residency Program Director, Department of Radiology, Massachusetts General Hospital, Harvard Medical School, Boston, Massachusetts
| | - James Milburn
- Residency Program Director, Department of Radiology, Ochsner Health System, New Orleans, Louisiana
| | - Megan K Mills
- Department of Radiology and Imaging Sciences, University of Utah, Salt Lake City, Utah
| | - Desiree E Morgan
- Vice-Chair of Education, Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Rustain Morgan
- Residency Program Director, Department of Radiology, University of Colorado Anschutz Medical Campus, Aurora, Colorado
| | - Ryan B Peterson
- Department of Radiology and Imaging Sciences, Emory University School of Medicine, Atlanta, Georgia
| | - Ninad Salastekar
- Department of Radiology, SUNY Upstate Medical University, Syracuse, New York
| | | | - Jessica G Zarzour
- Radiology Residency Program Director, Department of Radiology, University of Alabama at Birmingham, Birmingham, Alabama
| | - Shelby D Reed
- Department of Population Health Sciences, Duke University School of Medicine, Durham, North Carolina
| | - Lars J Grimm
- Department of Radiology, Duke University Medical Center, Durham, North Carolina
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