Barr DA: Science as superstition: selecting medical students. Lancet 376: 678, 2010.
Maher BM, Hynes H, Sweeney C, et al: Medical school attrition: beyond the statistics: a ten year retrospective study. BMC Med Educ 13: 13, 2013. Available at: http://www.biomedcentral.com/1472-6920/13/13. Accessed January 13, 2016.
O'Neill LD, Wallstedt B, Eika B, et al: Factors associated with dropout in medical education: a literature review. Med Educ 45: 440, 2011.
Yoho RM, Vardaxis V, Comstock K: Admission characteristics and academic performance of podiatric and osteopathic medical students at Des Moines University. JAPMA 100: 276, 2010.
Youngclaus J, Fresne JA: Physician education debt and the cost to attend medical school: 2012 update. Association of American Medical Colleges website. Available at: https://aamc-orange.global.ssl.fastly.net/production/media/filer_public/8d/aa/8daa5c2d-838b-4690-a353-170c7f4a7bab/physician_education_debt_and_the_cost_to_attend_medical_school_2012_update.pdf. Published February 2013. Accessed December 22, 2015.
Shaw GP, Velis E, Molnar D: Can we predict podiatric medical school grade point average using an admission screen? JAPMA 102: 499, 2012.
Sesodia S, Molnar D, Shaw GP: Can we predict 4-year graduation in podiatric medical school using admission data? JAPMA 102: 463, 2012.
Hurtado A: Impressions of the 1956 Institute on the evaluation of the student: the appraisal of applicants to medical school. J Med Educ 32: 847, 1957.
Albanese MA, Snow MH, Skochelak SE, et al: Assessing personal qualities in medical school admissions. Acad Med 78: 313, 2003.
Monroe A, Quinn E, Samuelson W, et al: An overview of the medical school admission process and use of applicant data in decision making: what has changed since the 1980s? Acad Med 88: 672, 2013.
Koenig TW, Parrish SK, Terregino CA, et al: Core personnel competencies important to entering students' success in medical school: what are they and how could they be assessed early in the admissions process? Acad Med 88: 603, 2013.
Adams AN, Bletzinga RB, Sondheimer HM, et al: Roadmap to Diversity: Integrating Holistic Review Practices Into Medical School Admissions Processes, Association of American Medical Colleges, Washington, DC, 2010.
Schwartzstein RM, Rosenfeld GC, Hilborn R, et al: Redesigning the MCAT exam: balancing multiple perspectives. Acad Med 88: 560, 2013.
Siu E, Reiter HI: Overview: what's worked and what hasn't as a guide towards predictive admissions tool development. Adv Health Sci Educ 14: 759, 2009.
Lievens F, Ones DS, Dilchert S: Personality scale validities increase throughout medical school. J Appl Psychol 94: 1514, 2009.
Wainer H: Estimating coefficients in linear models: it don't make no nevermind. Psychol Bull 83: 213, 1976.
Peskum C, Detsky A, Shandling M: Effectiveness of medical school admissions criteria in predicting residency ranking four years later. Med Educ 41: 57, 2007.
Mitchell KJ: Use of MCAT data in selecting students for admission to medical school. Med Educ 62: 871, 1987.
Blue AV, Gilbert GE, Elam CL, et al: Does institutional selectivity aid in the prediction of medical school performance. Acad Med 75: 31, 2000.
Clapp TT, Reid JC: Institutional selectivity as a predictor of applicant selection in medical school. J Med Educ 51: 850, 1976.
Wiley A, Koenig JA: The validity of the medical college admission test for predicting performance in the first two years of medical school. Acad Med 71: 83, 1996.
Kirchner GL, Holm MB: Prediction of academic and clinical performance of occupational therapy students in an entry-level master's program. Am J Occup Ther 51: 775, 1997.
Ross CA, Leichner P: Criteria for selecting residents: a reassessment. Can J Psychiatry 29: 681, 1984.
Shen H, Comrey AL: Predicting medical students' academic performances by their cognitive abilities and personality characteristics. Acad Med 72: 781, 1997.
Griffin B, Wilson I: Associations between the big five personality factors and multiple mini-interviews. Adv Health Sci Educ 17: 377, 2012.
Komarraju M, Karau SJ, Schmeck RR, et al: The Big Five personality traits, learning styles, and academic achievement. Pers Indiv Diff 51: 472, 2011.
John OP, Naumann IP, Soto J: “Paradigm Shift to the Integrative Big Five Trait Taxonomy: History, Measurement and Conceptual Issues,” in Handbook of Personality: Theory and Research, 3rd Ed, edited by OP John, RW Robbins, LA Pervin, p 114, Guilford Press, New York, 2008.
Poropat AE: A meta-analysis of the five-factor model of personality and academic performance. Psychol Bull 135: 322, 2009.
Duberstein P, Meldrum S, Fiscella K, et al: Influences on patients ratings of physicians: physicians demographics and personality. Patient Educ Couns 65: 270, 2007.
Duckworth AL, Peterson C, Mathews MD, et al: Grit: perseverance and passion for long term goals. J Pers Soc Psychol 92: 1087, 2007.
Duckworth AL, Quinn PD: Development and validation of the Short Grit Scale (GRIT-S). J Pers Assess 91: 166, 2009.
Cobb-Clark DA, Schurer S: The stability of big-five personality traits. Econ Lett 115: 11, 2012.
Attrition from medical school remains a serious cause of concern for the medical education community. Thus, there is a need to improve our ability to select only those candidates who will succeed at medical school from many highly qualified and motivated applicants. This can be achieved, in part, by reducing the reliance on cognitive factors and increasing the use of noncognitive character traits in high-stakes admissions decisions. Herein we describe an analytic rubric that combines research-derived predictors of medical school success to generate a composite score for use in admissions decisions. The analytic rubric as described herein represents a significant step toward evidenced-based admissions that will facilitate a more consistent and transparent qualitative evaluation of medical school applicants beyond their grades and Medical College Admissions Test scores and contribute to a redesigned and improved admissions process.