Predictors of USMLE Step 1 Score
http://journals.lww.com/academicmedicine/Fulltext/2003/05000/USMLE_Performances_in_a_Predominantly_Asian_and.10.aspx
USMLE Performances in a Predominantly Asian and Pacific Islander Population of Medical Students in a Problem-based Learning Curriculum
Kasuya, Richard T. MD, MSEd; Naguwa, Gwen S. MD; Guerrero, Anthony P.S. MD; Hishinuma, Earl S. PhD; Lindberg, Marlene A. PhD; Judd, Nanette K. PhD
Abstract
Purpose: To compare the USMLE performances of students of various ethnicities, predominantly Pacific Islander and Asian, at one medical school and to examine the predictive validity of MCAT scores for USMLE performance.
A total of 258 students in the graduating classes of 1996-2000 at the University of Hawai’i School of Medicine were classified by ethnicity. Demographic and performance characteristics of the groups were examined, and MCAT scores with and without undergraduate science GPA were used to predict USMLE performance. Under- and over-prediction rates were computed for each ethnic group.
Ethnic groups did not differ significantly by gender or undergraduate GPA. Chinese, Caucasian, and Other Asian students tended to have higher MCAT scores than Hawaiian/other Pacific Islander, and Filipino students. Ethnic groups did not differ significantly in prediction of USMLE Step 1 performance. For Step 2, MCAT scores significantly over-predicted performance of Filipino students and tended to under-predict performance of Caucasian students.
Conclusion: Although MCAT scores and science GPA were good predictors of USMLE performance, ethnic differences were found in the degrees of their predictive validity. These findings both replicate and extend results of earlier studies, and again point to the importance of exploring additional predictor variables. The authors encourage future research on the effects of the following factors on success in medical school: reading and test-taking skills, socio-cultural and environmental influences on learning, communication styles, primary language use, family support, and family responsibilities.
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More on the topic……….
http://www.ncbi.nlm.nih.gov/pubmed/15761831?ordinalpos=4&itool=EntrezSystem2.PEntrez.Pubmed.Pubmed_ResultsPanel.Pubmed_DefaultReportPanel.Pubmed_RVDocSum
Anat Rec B New Anat. 2005 Mar;283(1):5-8.
Medical gross anatomy as a predictor of performance on the USMLE Step 1.
Department of Physical Therapy, University of the Pacific, Stockton, California, USA.
Traditional predictors of medical school performance, such as Medical College Admission Test (MCAT) scores and grade point averages, are often used during the admissions process to help identify the prospective students who are most likely to complete the basic science portion of the curriculum successfully. Here we analyzed the admissions files and student records of 285 first-year medical students who matriculated at the University of California at Davis School of Medicine between 1999 and 2001 to determine if performance in medical gross anatomy is a similar, if not better, predictor of performance on the United States Medical Licensing Examination (USMLE) Step 1 than traditional predictors used by medical school admissions committees. Though MCAT scores and grade point averages were correlated with scores on the USMLE Step 1, only the score on the biological science section of the MCAT was significantly correlated with passing the licensing examination. In contrast, class rank in medical gross anatomy and the score on a gross anatomy comprehensive final examination were correlated both with scores on the USMLE Step 1 and passing the examination. Our results indicate that medical schools should consider performance in medical gross anatomy just as much, if not more, than traditional predictors of medical school performance when trying to identify students who may need more time or tutoring to pass the licensing examination. 2005 Wiley-Liss, Inc.
PMID: 15761831 [PubMed - indexed for MEDLINE]
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Adv Health Sci Educ Theory Pract. 2009 Mar;14(1):69-78. Epub 2007 Nov 7.
Impact of preadmission variables on USMLE step 1 and step 2 performance.
Department of Medicine, The University of Toledo College of Medicine, Health Science Campus, Toledo, OH 43614-2598, USA. James.Kleshinski@utoledo.edu
PURPOSE: To examine the predictive ability of preadmission variables on United States Medical Licensing Examinations (USMLE) step 1 and step 2 performance, incorporating the use of a neural network model. METHOD: Preadmission data were collected on matriculants from 1998 to 2004. Linear regression analysis was first used to identify predictors of performance on step 1 and step 2. A generalized regression neural network (GRNN) as well as a feed forward neural network (FFNN) was then developed in an effort to more accurately predict step 1 and step 2 scores from these preadmission data. RESULTS: Statistically significant predictors for step 1 and step 2 included science grade point average (SGPA), the biologic science (BS) section of the Medical College Admissions Test (MCAT), college selectivity, race, and age of the applicant. Neural networks were found to predict a significant portion of the variance, and the FFNN demonstrated some superiority over that obtained with linear regression models as well as the GRNN. CONCLUSIONS: The results have implications that could impact the selection of applicants to medical school and the neural networks that we developed could be used in a prospective manner.
PMID: 17987399 [PubMed - in process]
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