Using Simulation Modeling Approach To Predict Usmle Steps-Books Pdf

Using Simulation Modeling Approach to Predict USMLE Steps
28 Aug 2020 | 7 views | 0 downloads | 10 Pages | 724.49 KB

Share Pdf : Using Simulation Modeling Approach To Predict Usmle Steps

Download and Preview : Using Simulation Modeling Approach To Predict Usmle Steps

Report CopyRight/DMCA Form For : Using Simulation Modeling Approach To Predict Usmle Steps



Transcription

become important standard outcome measurements for seemed to be the preferred techniques in performing the. effective medical education prediction tasks 5 13 17 artificial intelligence models such. as Generalized Regression Neural Network GRNN and the. Passing the USMLE Step 1 is an important step in the medical multi layered Feed Forward Neural Network FFNN were. licensing process which leads to medical students being eligible the most sophisticated modeling approaches to discern the. to take subsequent examinations Steps 2 and 3 The Step 1 test pattern related to the USMLE Step 2 performances 17. score is widely used as a criterion for estimating the predictive. validity of the Medical College Admission Test MCAT and The simulation model is a sophisticated modeling tool. undergraduate grade point average GPA that are traditionally for quantifying the relative contribution of the uncertain input. used to screen medical school applicants for an admission independent variables to the overall variance and range of. interview 5 6 Because of the significant value for improving output dependent variable This process approximates the. medical education program and admission processes there have output variable through a random sampling algorithm The use of. been numerous studies investigating predictors of student simulation technology in medical education has significantly. performance on the USMLE Step 1 and utilizing modeling increased during the past decade The finding of a simulation. techniques to build the prediction models for licensure method confirmed that learning or mastery of skills actually. examination 7 12 occurs based on simulation results 18 Although medical. schools and related healthcare facilities have used this technique. Among the influencing factors on the USMLE Step 1 student to evaluate medical competencies across various domains such as. performance in the first two years of medical school is patient care medical knowledge practice based learning. considered the most prominent The pre admission variables communication skills and professionalism 19 using simulation. such as undergraduate GPA and MCAT scores are usually the models to predict medical licensure examination is still. two commonly used factors for building the prediction models underutilized To our best knowledge this paper generated the. 8 9 However one study showed a strong correlation first simulation model in predicting medical student licensure. between medical gross anatomy class rank and score with examination performances. both the scores on USMLE Step 1 and passing that exam. indicating that this subject should be added to the traditional 3 METHODOLOGY. predictors of medical school performance 10, The simulation process can be performed by using IBM SPSS. The vast majority of research studies were able to construct simulation software with a maximal sample size of. and interpret the functional relationship between various 100 000 simulation runs During this process samples are. predictors and student performance on Step 1 The extent to randomly selected with the parameters of specific probability. which MCAT scores predict USMLE Step 1 performance was distributions as well as their correlations. examined The study results demonstrated that the MCAT was. more strongly related to USMLE Step 1 than the undergraduate In the simulation model the correlation coefficients between. GPA 11 MCAT scores among 112 medical schools provided individual explanatory variables and the USMLE Steps 1 and 2. better predictions of the USMLE Step 1 performance than scores were displayed through the tornado chart The explanatory. undergraduate information alone 4 Thus MCAT scores variables were ranked in descending order based on the absolute. should continue to have substantial utility in the admission value of the correlation coefficients In another tornado chart the. process particularly in screening applicants to be interviewed change in the USMLE Steps 1 and 2 scores for plus or minus one. In addition the average MCAT score increase by one point led standard deviation change in explanatory variables showed the. to a 7 62 point increase in USMLE Step 1 score 12 sensitivity ratios of the USMLE Steps 1 and 2 scores. Examining the extent to which performance on the NBME. Comprehensive Basic Science Self Assessment a study Sensitivity analysis was also performed by identifying the most. provided more accurate basis for predicting USMLE Step 1 important explanatory variables in the model The explanatory. performance than NBME Basic Science Subject Board Tests 13 variables with the greatest impact on the USMLE Steps 1 and 2. 16 scores were considered the key explanatory variables Sensitivity. analysis was used to vary the model results under plausible values. Medical students in the United States are required to pass the of parameter change on a key explanatory variable holding others. USMLE Step 2 to be placed in a residency program Previous constant This analysis was applied because it could enhance the. studies focused on academic variables that were successful in understanding of the USMLE Steps 1 and 2 performances. predicting the USMLE Step 2 score Variables having positive. through its linkages with explanatory variables It is anticipated. associations with the USMLE Step 2 score included Basic. that the study results could determine the consistency of the slopes. Science GPA MCAT Biological Science score and race 17. in linear regression and the sensitivity ratios in the sensitivity. Medical school performances in the first three years the. USMLE Step 1 score and the NBME Comprehensive Clinical analysis Therefore the simulation model via linear regression. Science Assessment CCSSA scores 13 18 were also could provide the College s decision makers with the evidence. strong indicators for predicting Step 2 performance In based information leading to effective intervention strategies. addition a positive linear relationship was evident between. the USMLE Step 2 score and both Family Medicine and Medical students with the complete records available in. Clinical Evaluation scores 5 13 matriculation years 2010 2013 n 313 for Step 1 prediction and. n 196 for Step 2 prediction were selected for data analysis using. Various statistical techniques such as Pearson s correlation linear regression Also a random selection of 1 000 simulation. coefficient t test and ANOVA were commonly used to detect run stochastic process was executed to form another sample. significant variables affecting the USMLE Steps 1 and 2 chosen for independent sample and equal probability in the. scores 17 Although simple and multiple regression analyses simulation models. 68 SYSTEMICS CYBERNETICS AND INFORMATICS VOLUME 15 NUMBER 1 YEAR 2017 ISSN 1690 4524. Pathology Subject Board scores were positively and significantly. 4 STUDY VARIABLES associated with the USMLE Step 1 performance with p value less. than the 01 significance level Also the NBME Pharmacology. The purpose of this study was to assess whether prediction models score positively and significantly contributed to the USMLE Step. based on the MCAT scores and student performances on all 1 performance with p value less than the 05 significance level. required NBME Basic Science Subject Board courses in the The NBME Comprehensive Basic Science Subject Board Score. medical school curriculum could accurately predict performances from April examination positively and significantly impacted the. of USMLE Steps 1 and 2 The outcome variables for this study Step 1 performance with p value less than the 001 significance. were the USMLE Steps 1 and 2 first time taker scores Fifteen level However the following variables had no effect on the. variables were treated as independent variables for the USMLE USMLE Step 1 performance race gender basic science GPA. Step 1 gender 1 male 0 female ethnicity 1 African undergraduate GPA MCAT Verbal Reasoning Physical. American 0 Non African American undergraduate GPA, Science and Biological Science Scores and NBME Basic. MCAT scores Biological Sciences Physical Sciences and. Science Subject Board Scores in Biochemistry Microbiology. Verbal Reasoning and NBME Basic Science Subject Board. Physiology and Comprehensive Basic Science Subject Board. scores Anatomy Biochemistry Microbiology Pathology. Pharmacology and Physiology As shown in Table I sixteen from January examination. variables were considered as independent variables for the. USMLE Step 2 which included the USMLE Step 1 score and TABLE II. fifteen variables mentioned above LINEAR REGRESSION FOR USMLE STEP 1. TABLE I Unstandardized Standardized, Variables in, STUDY VARIABLES Coefficients Coefficients P Value. or Slope Beta, RACE GRP 3 559 070 052, Variable Names Variable Descriptions GENDER GRP 087 002 961. Race Group 1 African American 0 BS GPA 2 104 041 605. Non African American UG GPA 1 392 021 784, GENDER GRP Gender Group 1 Male 0 Female MCAT VR 163 013 677.
BS GPA Undergraduate Science GPA MCAT PS 030 002 947. UG GPA Undergraduate GPA MCAT BS 010 001 985, Anatomy and. MCAT VR MCAT Verbal Reasoning Score 240 107 007, Embryology. MCAT PS MCAT Physical Science Score Biochemistry 153 075 079. Microbiology 216 087 062, MCAT BS MCAT Biological Science Score. Pathology 184 127 002, NBME Anatomy and Embryology Subject. AnatEmbry Pharmacology 156 092 035, Board Score, Physiology 109 057 278.
Biochemistry NBME Biochemistry Subject Board Score. Comp BS Jan 056 021 676, Microbiology NBME Microbiology Subject Board Score. Comp BS Apr 1 035 506 000, Pathology NBME Pathology Subject Board Score. p 05 p 01 and p 0 001, Pharmacology NBME Pharmacology Subject Board Score. The data analysis was first performed by displaying the. Physiology NBME Physiology Subject Board Score probability distributions and related parameters of input. NBME Comprehensive Subject Board variables The probability distributions consisted of normal. Comp BS Jan distributions for three NBME Basic Science Subject Board scores. Score from January Examination, Anatomy and Embryology Biochemistry Microbiology and. NBME Comprehensive Subject Board MCAT Verbal Reasoning three lognormal distributions for the. Comp BS Apr, Score from April Examination NBME Basic Science Subject Board Pathology score MCAT.
Physical Science score and Undergraduate GPA two gamma. Step1 Score USMLE Step 1 Score, distributions for NBME Basic Science Subject Board. Pharmacology and Physiology scores and two triangular. distributions for gender and race groups, 5 STUDY RESULTS. Range Estimations of USMLE Step 1 As shown in Fig 1. USMLE Step 1 Prediction Results The study attempted below median 50th percentile of all students had a USMLE. to find the association between USMLE Step 1 performance and Step 1 score equal to 216 13 Also 25 of all students had. its predictors under the investigation Of the fifteen predictors USMLE Step 1 score over 204 37 and only 5 of all students. used in the model the NBME Anatomy Embryology and had USMLE Step 1 score greater than 246 26. ISSN 1690 4524 SYSTEMICS CYBERNETICS AND INFORMATICS VOLUME 15 NUMBER 1 YEAR 2017 69. Outcome 95 Confidence, Changes Interval for USMLE, Input USMLE Step 1 mean Score. Fig 1 Probability Density Function for USMLE Step 1 Input Variable Step 1. Lower Upper, Variable Changes Score, Variables Importance for USMLE Step 1 As shown in NBME. Anatomy 59 175 216 364 215 274 217 455, Fig 2 the NBME Comprehensive Basic Science Subject Board.
score from April administration was the highest correlated and 64 175 218 213 217 073 219 354. explanatory variable that contributed to the USMLE Step 1 score Embry. 69 175 218 654 217 579 219 729, r 0 95 followed by the NBME Physiology Subject Board Score. score r 0 82 and the NBME Comprehensive Basic Science. Subject Board score from January exam r 0 78 Fig 3 Probability Density Function Based on the Increment. of NBME Anatomy Embryology Subject Board Score, The study results showed that if the NBME Pathology increased. by five points from 66 444 to 71 444 the USMLE Step 1 would. increase by two points from 216 364 to 217 863 However. additional five point increments from 71 444 to 76 444 of the. NBME Pathology score would only result in a slight increase. Fig 2 Tornado Chart for Correlations with USMLE Step 1. Outcome 95 Confidence, Sensitivity Analysis for USMLE Step 1 Score The study Changes Interval for USMLE. results showed that if the NBME Anatomy Embryology score Input USMLE Step 1 Mean Score. increased by five points from 59 175 to 64 175 the USMLE Input Variable Step 1. Lower Upper, Step 1 score would increase by two points from 216 364 to Variable Changes Score. 218 213 However additional five point increments from 66 444 216 364 215 274 217 455. 64 175 to 69 175 of the NBME Anatomy Embryology score. Pathology 71 444 217 863 216 715 219 011, would only result in a slight increase of less than one point from.
Score 76 444 217 943 216 852 219 033, 218 213 to 218 654. Fig 4 Probability Density Function Based on the Increment. of NBME Pathology Subject Board Score, 70 SYSTEMICS CYBERNETICS AND INFORMATICS VOLUME 15 NUMBER 1 YEAR 2017 ISSN 1690 4524. The study results showed that if the NBME Pharmacology score. increased by five points from 65 206 to 70 206 the USMLE. Step 1 score would increase by one point from 216 364 to. 217 790 However additional five point increments from. 70 208 to 75 208 of the NBME Pharmacology score would only. Using Simulation Modeling Approach to Predict USMLE Steps 1 and 2 Performances Chau Kuang Chen Office of Institutional Research Meharry Medical College Nashville TN 37208 USA John Hughes Jr Office of Institutional Research Meharry Medical College Nashville TN 37208 USA and A Dexter Samuels Division of Student Affairs Meharry Medical College Nashville TN 37208 USA ABSTRACT The

Related Books

Basics of Photovoltaic PV Systems for Grid Tied Applications

Basics of Photovoltaic PV Systems for Grid Tied Applications

Basics of Photovoltaic PV Systems for Grid Tied Applications Pacific Energy Center EnergyT raining Center 851 Howard St 1129 Enterprise St San Francisco CA 94103 Stockton CA 95204 Courtesy of DOE NREL instructor Pete Shoemaker

PIN SSOP PHOTOTRANSISTOR PHOTOCOUPLER A PLER E

PIN SSOP PHOTOTRANSISTOR PHOTOCOUPLER A PLER E

T PHO Series response ansfer ratio in 20 at I F tion voltage ut Viso 3 small outline nd RoHS co ved No E2 roved No 1 pproved pproved pproved pproved oved G series co a phototrans ed in a 4 pi s onitor able contro e line interfa polarity DC ll Rights Reserv OTOTR TOCOU 1mA V C between inp 750 V rms package mpliant 14129 32249 ntains two i istor encaps n small outlin llers ce

MENU Menu Setup Lexus

MENU Menu Setup Lexus

Please refer to the 2017 RX 350 Quick Guide or Owner s Manual for more information on Display Audio operations Door Lock Settings 1 Automatic Door Lock The automatic door locks can be programmed as follows The doors automatically lock when the vehicle speed is 12 mph or higher By speed The doors automatically lock when the vehicle is taken out of Park and shifted into another range

Brochure for 2013 Lexus RX amp RXh Hybrid

Brochure for 2013 Lexus RX amp RXh Hybrid

As the very first luxury crossover the Lexus RX invented a language of its own With its innovative approach to design safety and utility the RX interpreted the needs of discerning drivers into a new expression of luxury For 2013 Lexus continues to evolve the RX adding the all new performance oriented RX FSPORT to a model line that includes the RX350 and the RX450h the latest iteration

How nationalism can promote democracy evidence from South

How nationalism can promote democracy evidence from South

Keywords nationalism democracy inclusivity citizenship stabilit y Pleasedirect any correspondencerelating to this BSG Working Paper to maya tudor bsg ox ac uk 1 Anearlier versionof this article is currently under review as part of aneditedvolume with Cambridge University Press 2 I IntroductionandArguments India and Indonesia are the two largest and unlikeliest

Democratic Nationalism and Multiculutralism Democracy

Democratic Nationalism and Multiculutralism Democracy

Democratic Nationalism and Multicultural Democracy i Meindert Fennema and Jean Tillie NIAS March 2001 e mail fennema nias knaw nl Second draft Paper prepared for the workshop on Immigration Integration and the European Union at the Joint Session of Workshops of the ECPR in Grenoble 6 11 April 2001 2 1 0 Abstract This paper sets out to investigate the relationship between nationalism and

Nationalism and democracy final draft

Nationalism and democracy final draft

towards democracy Nationalism was one of the most effective political forces of the 20th century In Europe already established democratic nation states so I focus on national identity and not collective political behaviour However in the conclusion section when I discuss wider implications of the main findings I comment on the relationship between national identity and actual

Nationalism and Democracy Competing or Complementary Logics

Nationalism and Democracy Competing or Complementary Logics

that nationalism and democracy might also be considered as mutually dependent logics Introduction1 Upon initial consideration the logics of nationalism and democracy seem to be contradictory Nationalism appears to be predicated upon a doctrine of exclusivity whereas democracy seems to be based on an inclusivist one Upon careful

Octavia E Butler Humanities Ebooks

Octavia E Butler Humanities Ebooks

Octavia E Butler Xenogenesis Lilith s Brood John Lennard Tirril Humanities Ebooks 2007 A Note on the Author John Lennard took his B A and D Phil at Oxford University and his M A at Washington University in St Louis He has taught in the Universities of London Cambridge and Notre Dame and for the Open University and is now Professor of British amp American Literature at the

Contents DAWN IN MZANSI We Do It Better

Contents DAWN IN MZANSI We Do It Better

BOOKS BY OCTAVIA E BUTLER Fledgling Parable of the Talents Parable of the Sower Lilith s Brood Dawn Adulthood Rites Imago Seed to Harvest Wild Seed Mind of My Mind Clay s Ark Patternmaster Kindred Survivor Bloodchild and Other Stories available from Warner Books

7 SISTEMA FINANCIERO EN LA COMUNITAT VALENCIANA N MERO DE

7 SISTEMA FINANCIERO EN LA COMUNITAT VALENCIANA N MERO DE

N MERO DE ENTIDADES DE CR DITO Y DE EMPLEADOS EN ESPA A 2015 2016 IV Trimestre Entidades Empleados Entidades Empleados Entidades Empleados Entidades de Dep sito 217 197 825 206 189 280 5 3 4 3 Espa olas 135 124 8 9 Extranjeras 82 82 0 0 Establec Financ de cr dito EFC 44 4 812 43 4 695 2 3 2 4 Cr dito oficial 1 317 1 308 0 0 2 8 Total Entidades de Cr dito 262 202 954 250 194 283