Does the MCAT Predict Medical School and PGY-1 Performance?

Saguil, Aaron; Dong, Ting; Gingerich, Robert J.; Swygert, Kimberly A.; LaRochelle, Jeffrey; Artino, Anthony R.; Cruess, David F.; Durning, Steven J.

doi:10.7205/milmed-d-14-00550

Cited by 46 publications

(31 citation statements)

References 14 publications

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“…These authors found that for each point increase in the MCAT science score, there was a 4.26-point increase in the Step 1 score [ 6 ]. These findings were consistent with other studies, which have demonstrated a correlation of MCAT scores to Step 1 scores [ 7 - 8 ].…”

Section: Introductionsupporting

confidence: 93%

A Predictive Model for USMLE Step 1 Scores

Giordano¹,

Hutchinson²,

Peppler³

2016

Cureus

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PurposeThe United States Medical Licensing Examination (USMLE) Step 1 plays a pivotal role in one’s residency application. While prior literature has investigated which factors influence performance on the examination, the authors sought to include factors such as performance on a well-used question bank and financial need to develop a predictive model.Method After obtaining institutional review board approval, the authors surveyed two consecutive second-year medical school classes and correlated the data to the students’ Step 1 and National Board of Medical Examiners (NBME) Comprehensive Basic Science Examination (CBSE) scores. The survey included questions such as how many days they studied per week, how many days they studied in total, which resources they used, how they performed on question banks, group studying habits, and whether they were receiving financial aid. The authors also assessed whether the students received only A letter grades during the first year of medical school. The authors used SPSS® Statistics V22.0 (IBM® Corporation, NY, USA ) and included one-way analysis of covariance (ANOVA) and multiple linear regression for statistical analysis.Results Eighty-one students completed the survey with an average Step 1 score of 240.5 and with an average study time of 39.5 days. The Step 1 Scores significantly correlated with the CBSE taken immediately preceding the dedicated study period (r=0.711, P=<0.001), UWorld Question Bank (UWorld) percentage correct (r = 0.622, P<0.001), straight As during first-year (r=0.356, P=0.001), and financial need (r=0.318, P=0.01). The scores were not correlated with age, gender, Medical College Admissions Test (MCAT), prior medical training, number of days studied, or the students’ perception of appropriate time studied. The authors developed a predictive model accounting for 62.3% of the variability. 140.625+(0.319xCBSE)-(3.817xA)+(5.845xN)+(0.452xU), where A=1 if straight As, N=1 if receiving need-based scholarship, U=UWorld percent-correct, and CBSE=the three-digit score of the CBSE taken prior to the dedicated study period.Conclusions Academic performance and financial need may predict Step 1 scores. Interestingly, the number of days studied did not have a correlation with scores, suggesting that increased length of study may not ameliorate poor grades.

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Section: Introductionsupporting

confidence: 93%

A Predictive Model for USMLE Step 1 Scores

Giordano¹,

Hutchinson²,

Peppler³

2016

Cureus

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“…It is well recognised that pre-tertiary academic performance predicts university academic results, 11,19,20 although the strength of this association diminishes over time, 12 such that prior academic performance is less a predictor of performance in the clinical years. 21 Accordingly, the association between pre-entry academic success and clinical performance (KSA) was less than was observed for the GPA in the current study.…”

Section: Discussioncontrasting

confidence: 73%

Student selection to a rural veterinary school. 2: predictors of student performance and attrition

et al. 2019

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Objective To evaluate the extent to which current selection processes at Charles Sturt University, New South Wales, Australia, predict performance within and attrition from the veterinary degree program. Design Retrospective evaluation of application details and student performance data. Methods Database records of 424 students entering the veterinary program were retrieved from university records, including all students graduating (n = 356) or leaving the program without completing (n = 68) between 2005 and 2016. Demographic data were related to results of selection processes and achievement within the degree using univariable and multivariable general linear and logistic regression analyses. Results The grade point average achieved over the 6 years of the degree was influenced by academic achievement prior to entry, gender (females performed ≈ 2% better than males) and interview scores. Preceptor evaluation of final‐year clinical performance was associated with interview scores, gender (males performed ≈ 4% better than females) and residential address at the time of application. Attrition for personal reasons was more common for female students and students who had completed a prior degree, and students who experienced academic or personal attrition had lower written application scores. Factors that have been previously linked to poor academic outcomes, including rural or low socioeconomic background, were not associated with adverse student outcomes. Conclusion Selection processes did not systematically disadvantage students admitted to the degree program. Gender differences in personal attrition and academic and clinical performance warrant further evaluation. Factors in addition to academic aptitude predicted student success.

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“…Given this highly competitive process, it can be challenging for medical school admissions committees to identify and select those applicants that are most promising and that best align with the stated missions of the individual school [1]. During some stages of this process, candidates may be considered collectively as a homogeneous population for whom generalized cutoffs for performance in standardized tests and undergraduate grade point average (uGPA) are used [2][3][4][5][6][7][8][9][10][11][12][13].…”

Section: Introductionmentioning

confidence: 99%