scholarly journals Using Markov chain model to evaluate medical students’ trajectory on progress tests and predict USMLE step 1 scores---a retrospective cohort study in one medical school

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Ling Wang ◽  
Heather S. Laird-Fick ◽  
Carol J. Parker ◽  
David Solomon
Keyword(s):  
Markov Chain ◽  
Medical Students ◽  
Medical School ◽  
Markov Chain Model ◽  
First Year ◽  
Chain Model ◽  
Progress Test ◽  
Usmle Step 1 ◽  
Licensing Examination ◽  
Usmle Step

Abstract Background Medical students must meet curricular expectations and pass national licensing examinations to become physicians. However, no previous studies explicitly modeled stages of medical students acquiring basic science knowledge. In this study, we employed an innovative statistical model to characterize students’ growth using progress testing results over time and predict licensing examination performance. Methods All students matriculated from 2016 to 2017 in our medical school with USMLE Step 1 test scores were included in this retrospective cohort study (N = 358). Markov chain method was employed to: 1) identify latent states of acquiring scientific knowledge based on progress tests and 2) estimate students’ transition probabilities between states. The primary outcome of this study, United States Medical Licensing Examination (USMLE) Step 1 performance, were predicted based on students’ estimated probabilities in each latent state identified by Markov chain model. Results Four latent states were identified based on students’ progress test results: Novice, Advanced Beginner I, Advanced Beginner II and Competent States. At the end of the first year, students predicted to remain in the Novice state had lower mean Step 1 scores compared to those in the Competent state (209, SD = 14.8 versus 255, SD = 10.8 respectively) and had more first attempt failures (11.5% versus 0%). On regression analysis, it is found that at the end of the first year, if there was 10% higher chance staying in Novice State, Step 1 scores will be predicted 2.0 points lower (95% CI: 0.85–2.81 with P < .01); while 10% higher chance in Competent State, Step 1scores will be predicted 4.3 points higher (95% CI: 2.92–5.19 with P < .01). Similar findings were also found at the end of second year medical school. Conclusions Using the Markov chain model to analyze longitudinal progress test performance offers a flexible and effective estimation method to identify students’ transitions across latent stages for acquiring scientific knowledge. The results can help identify students who are at-risk for licensing examination failure and may benefit from targeted academic support.

scholarly journals Using Markov Chain Model to Evaluate Medical Students’ Trajectory on Progress Tests and Predict USMLE Step 1 Scores

2021 ◽  
Author(s):  
Ling Wang ◽  
Heather S. Laird-Fick ◽  
Carol J. Parker ◽  
David Solomon
Keyword(s):  
Markov Chain ◽  
Medical Students ◽  
Scientific Knowledge ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Estimation Method ◽  
First Year ◽  
Chain Model ◽  
Progress Test ◽  
Licensing Examination

Abstract Medical students must meet curricular expectations and pass national licensing examinations to become physicians. The Michigan State University College of Human Medicine implemented progress testing in place of discipline-specific examinations as its primary assessment of knowledge in 2016. Ideally this innovative assessment strategy will characterize students’ growth in basic science knowledge over time and predict licensing examination performance.Markov chain method was employed to: 1) identify latent states of acquiring scientific knowledge based on progress tests, 2) estimate students’ transition probabilities between states, and 3) predict United States Medical Licensing Examination Step 1 results based on the students’ predicted probabilities in each state. A total of 358 students were included in the analysis. Four latent states were identified based on students’ progress test results: Novice, Advanced Beginner I, Advanced Beginner II and Competent States. At the end of the first year, students predicted to remain in the Novice state had lower mean Step 1 scores compared to those in the Competent state (209, SD = 14.8 versus 255, SD = 10.8 respectively) and had more first attempt failures (11.5% versus 0%). On regression analysis, it is found that at the end of the first year, if there was 10% higher chance staying in Novice State, Step 1 scores will be predicted 2.0 points lower (P< .01); while 10% higher chance in Competent State, Step 1scores will be predicted 4.3 points higher (P< .01). Similar findings were also found at the end of second year medical school.Using the Markov chain model to analyze longitudinal progress test performance offers a flexible and effective estimation method to identify students’ transitions across latent stages for acquiring scientific knowledge. The results can help identify students who are at-risk for licensing examination failure and may benefit from targeted academic support.

scholarly journals The WRITE Stuff: A Rural Longitudinal Integrated Clerkship Addresses Workforce Needs

2019 ◽  
Vol 2 (5) ◽  
Author(s):  
Misbah Keen ◽  
Danielle Bienz ◽  
Toby Keys ◽  
Douglas Schaad ◽  
David Evans
Keyword(s):  
Primary Care ◽  
Medical Students ◽  
Family Medicine ◽  
Medical School ◽  
First Year ◽  
Key Word ◽  
Usmle Step 1 ◽  
Longitudinal Integrated Clerkship ◽  
Usmle Step ◽  
Workforce Needs

Introduction: The University of Washington School of Medicine has six campuses in the five state WWAMI (Washington, Wyoming, Alaska, Montana and Idaho) region. The WRITE (WWAMI Rural Integrated Training Experience) program is a 22 to 24 week long rural longitudinal integrated clerkship experience offered to medical students in their clinical phase (third year) of training. This program seeks to meet the rural workforce needs of the WWAMI region by increasing the number of medical students going into primary care. Critics of LIC’s have expressed concern about overall quality control of the more remote educational experience and the lack of specialty specific teaching.  The aim of this study was to compare medical school and PGY-1 performance of WRITE and Non-WRITE students while determining how well each cohort is meeting the regional workforce needs. Methods: The study group was all UWSOM students who matriculated from 2009 to 2013, advanced to graduation, and subsequently matched to a residency through the National Residency Match Program. WRITE and non-WRITE cohorts were compared for USMLE step 1 and 2 scores, MSPE (Medical Student Performance Evaluation) key word, and self and program director assessments in the first year of residency. The match results of the two cohorts were also compared to determine the proportions entering primary care residencies. Finally, for both cohorts the specialty choice at matriculation was compared with the match results. Descriptive statistics were used to test the comparisons. Results: The medical school performance of the WRITE and Non-WRITE cohorts was equivalent in all metrics (USMLE Step 1 and 2, MSPE key word, self and program director assessment of performance in the first year of residency). WRITE students were significantly more likely to match into primary care (67.6% vs 48.3%, p<0.001) overall and, in particular, Family Medicine as their specialty (40% vs 14.3%, p<0.001).  WRITE students were also more likely to match into the same specialty that they indicated on the UWSOM matriculation survey. For Family Medicine the loss of fidelity between matriculation and match among WRITE students was 3% (43.4 - 40.4) and among Non-WRITE students, it was 6.3% (20.6 - 14.3). Conclusions: Performance outcomes of the WRITE program are equivalent to a traditional block curriculum.  However, the WRITE cohort is significantly more likely to go into primary care fields, especially family medicine and is more likely to stay with the declared specialty at matriculation. Medical schools that seek to increase the number of students going into primary care may benefit from adopting a similar model.

scholarly journals Understanding the Interview and Ranking Behaviors of Unmatched International Medical Students and Graduates in the 2013 Main Residency Match

2015 ◽  
Vol 7 (4) ◽  
pp. 610-616 ◽  
Author(s):  
Mei Liang ◽  
Laurie S. Curtin ◽  
Mona M. Signer ◽  
Maria C. Savoia
Keyword(s):  
Rank Order ◽  
International Medical ◽  
First Year ◽  
School Students ◽  
The Past ◽  
Medical Licensing ◽  
Usmle Step 1 ◽  
Licensing Examination ◽  
Usmle Step ◽  
Residency Match

ABSTRACT Background  Over the past decade, the number of unfilled positions in the National Resident Matching Program (NRMP) Main Residency Match has declined by one-third, while the number of unmatched applicants has grown by more than 50%, largely due to a rise in the number of international medical school students and graduates (IMGs). Although only half of IMG participants historically have matched to a first-year position, the Match experiences of unmatched IMGs have not been studied. Objective  We examined differences in interview and ranking behaviors between matched and unmatched IMGs participating in the 2013 Match and explored strategic errors made by unmatched IMGs when creating rank order lists. Methods  Rank order lists of IMGs who failed to match were analyzed in conjunction with their United States Medical Licensing Examination (USMLE) Step 1 scores and responses on the 2013 NRMP Applicant Survey. IMGs were categorized as “strong,” “solid,” “marginal,” or “weak” based on the perceived competitiveness of their USMLE Step 1 scores compared to other IMG applicants who matched in the same specialty. We examined ranking preferences and strategies by Match outcome. Results  Most unmatched IMGs were categorized as “marginal” or “weak”. However, unmatched IMGs who were non-US citizens presented more competitive USMLE Step 1 scores compared to unmatched IMGs who were US citizens. Unmatched IMGs were more likely than matched IMGs to rank programs at which they did not interview and to rank programs based on their perceived likelihood of matching. Conclusions  The interview and ranking behaviors of IMGs can have far-reaching consequences on their Match experience and outcomes.

scholarly journals Objective predictors of intern performance

2021 ◽  
Vol 21 (1) ◽  
Author(s):  
Amanda C. Filiberto ◽  
Lou Ann Cooper ◽  
Tyler J. Loftus ◽  
Sonja S. Samant ◽  
George A. Sarosi ◽  
...  
Keyword(s):  
Medical Students ◽  
Medical School ◽  
Core Competencies ◽  
Cohort Analysis ◽  
The United States ◽  
Grade Point ◽  
Class Rank ◽  
Residency Programs ◽  
Usmle Step 1 ◽  
Usmle Step

Abstract Background Residency programs select medical students for interviews and employment using metrics such as the United States Medical Licensing Examination (USMLE) scores, grade-point average (GPA), and class rank/quartile. It is unclear whether these metrics predict performance as an intern. This study tested the hypothesis that performance on these metrics would predict intern performance. Methods This single institution, retrospective cohort analysis included 244 graduates from four classes (2015–2018) who completed an Accreditation Council for Graduate Medical Education (ACGME) certified internship and were evaluated by program directors (PDs) at the end of the year. PDs provided a global assessment rating and ratings addressing ACGME competencies (response rate = 47%) with five response options: excellent = 5, very good = 4, acceptable = 3, marginal = 2, unacceptable = 1. PDs also classified interns as outstanding = 4, above average = 3, average = 2, and below average = 1 relative to other interns from the same residency program. Mean USMLE scores (Step 1 and Step 2CK), third-year GPA, class rank, and core competency ratings were compared using Welch’s ANOVA and follow-up pairwise t-tests. Results Better performance on PD evaluations at the end of intern year was associated with higher USMLE Step 1 (p = 0.006), Step 2CK (p = 0.030), medical school GPA (p = 0.020) and class rank (p = 0.016). Interns rated as average had lower USMLE scores, GPA, and class rank than those rated as above average or outstanding; there were no significant differences between above average and outstanding interns. Higher rating in each of the ACGME core competencies was associated with better intern performance (p < 0.01). Conclusions Better performance as an intern was associated with higher USMLE scores, medical school GPA and class rank. When USMLE Step 1 reporting changes from numeric scores to pass/fail, residency programs can use other metrics to select medical students for interviews and employment.

Use of the NBME Comprehensive Basic Science Examination as a progress test in the preclerkship curriculum of a new medical school

2014 ◽  
Vol 38 (4) ◽  
pp. 315-320 ◽  
Author(s):  
Teresa R. Johnson ◽  
Mohammed K. Khalil ◽  
Richard D. Peppler ◽  
Diane D. Davey ◽  
Jonathan D. Kibble
Keyword(s):  
Medical School ◽  
Basic Science ◽  
External Validation ◽  
Multiple Choice ◽  
The United States ◽  
Progress Test ◽  
Formal Curriculum ◽  
Usmle Step 1 ◽  
Usmle Step ◽  
Over Time

In the present study, we describe the innovative use of the National Board of Medical Examiners (NBME) Comprehensive Basic Science Examination (CBSE) as a progress test during the preclerkship medical curriculum. The main aim of this study was to provide external validation of internally developed multiple-choice assessments in a new medical school. The CBSE is a practice exam for the United States Medical Licensing Examination (USMLE) Step 1 and is purchased directly from the NBME. We administered the CBSE five times during the first 2 yr of medical school. Student scores were compared with scores on newly created internal summative exams and to the USMLE Step 1. Significant correlations were observed between almost all our internal exams and CBSE scores over time as well as with USMLE Step 1 scores. The strength of correlations of internal exams to the CBSE and USMLE Step 1 broadly increased over time during the curriculum. Student scores on courses that have strong emphasis on physiology and pathophysiology correlated particularly well with USMLE Step 1 scores. Student progress, as measured by the CBSE, was found to be linear across time, and test performance fell behind the anticipated level by the end of the formal curriculum. These findings are discussed with respect to student learning behaviors. In conclusion, the CBSE was found to have good utility as a progress test and provided external validation of our new internally developed multiple-choice assessments. The data also provide performance benchmarks both for our future students to formatively assess their own progress and for other medical schools to compare learning progression patterns in different curricular models.

Implications of the United States Medical Licensing Examination Step 1 Examination Transition to Pass/Fail on Medical Students Education and Future Career Opportunities

2020 ◽  
pp. 000313482097338
Author(s):  
Haley Ehrlich ◽  
Mason Sutherland ◽  
Mark McKenney ◽  
Adel Elkbuli
Keyword(s):  
United States ◽  
Medical Students ◽  
The United States ◽  
Residency Programs ◽  
Future Career ◽  
Career Opportunities ◽  
Medical Licensing ◽  
Usmle Step 1 ◽  
Licensing Examination ◽  
Usmle Step

Background United States Medical Licensing Examination (USMLE) Step 1 will transition to pass/fail score by 2022. We aim to investigate US medical students’ perspectives on the potential implications this transition would have on their education and career opportunities. Methods A cross-sectional study investigating US medical students’ perspectives on the implications of transition of the USMLE Step 1 exam to pass/fail. Students were asked their preferences regarding various aspects of the USMLE Step 1 examination, including activities, educational opportunities, expenses regarding preparation for the examination, and future career opportunities. Results 215 medical students responded to the survey, 59.1% were women, 80.9% were allopathic vs. 19.1% osteopathic students. 34.0% preferred the USMLE Step 1 to be graded on a pass/fail score, whereas 53.5% preferred a numeric scale. Osteopathic vs. allopathic students were more likely to report that the pass/fail transition will negatively impact their residency match (aOR = 1.454, 95% CI: 0.515, 4.106) and specialty of choice (aOR = 3.187, 95% CI: 0.980, 10.359). 57.7% of respondents reported that the transition to a pass/fail grading system will change their study habits. Conclusions The transition of the USMLE Step 1 to a pass/fail system has massive implications on medical students and residency programs alike. Though the majority of medical students did not prefer the USMLE Step 1 to have a pass/fail score, they must adapt their strategies to remain competitive for residency applications. Residency programs should create a composite score based off all aspects of medical students’ applications in order to create a holistic and fair evaluation and ranking system.

scholarly journals Ophthalmology Program Directors' Perspectives on the Impact of the United States Medical Licensing Examination Step 1 Change to Pass-Fail Scoring

2020 ◽  
Vol 12 (02) ◽  
pp. e277-e283
Author(s):  
David Cui ◽  
Ingrid U. Scott ◽  
Heidi Luise Wingert
Keyword(s):  
United States ◽  
Medical Education ◽  
Medical Students ◽  
The United States ◽  
Basic Science Knowledge ◽  
Medical Licensing ◽  
Usmle Step 1 ◽  
Licensing Examination ◽  
Usmle Step ◽  
The Impact

Abstract Purpose This article investigates the perspectives of ophthalmology residency program directors (PDs) regarding the impact of the United States Medical Licensing Examination (USMLE) Step 1 change from graded to pass-fail scoring on ophthalmology resident selection and medical education. Methods The PDs of all United States ophthalmology residency programs accredited by the Accreditation Council for Graduate Medical Education were identified using a public, online database. An anonymous web-based survey constructed using REDCap was emailed to each PD in February 2020. Results Surveys were completed by 64 (54.2%) PDs, with the majority (81.2%) disagreeing with the change to pass-fail scoring. The majority of PDs believe this change will negatively impact the ability to evaluate residency applicants (92.1%) and achieve a fair and meritocratic match process (76.6%), and will decrease medical students' basic science knowledge (75.0%). The factors identified most frequently by PDs as becoming more important in evaluating residency applicants as a result of the Step 1 scoring change include clerkship grades (90.6%), USMLE Step 2 Clinical Knowledge score (84.4%), and a rotation in the PD's department (79.7%). The majority of PDs believe the Step 1 grading change to pass-fail will benefit applicants from elite medical schools (60.9%), and disadvantage applicants from nonelite allopathic schools (82.8%), international medical graduate applicants (76.6%), and osteopathic applicants (54.7%). Conclusion The majority of ophthalmology PDs disagree with the change in USMLE Step 1 scoring from graded to pass-fail and believe this change will negatively impact the ability to evaluate residency applicants and achieve a fair and meritocratic match process, and will decrease medical students' basic science knowledge.

An Efficient Markov Chain Model for the Simulation of Heterogeneous Soil Structure

2004 ◽  
Vol 68 (2) ◽  
pp. 346 ◽  
Author(s):  
Keijan Wu ◽  
Naoise Nunan ◽  
John W. Crawford ◽  
Iain M. Young ◽  
Karl Ritz
Keyword(s):  
Markov Chain ◽  
Soil Structure ◽  
Markov Chain Model ◽  
Chain Model ◽  
Heterogeneous Soil

Data Mining Technique Applied To DNA Sequencing

2015 ◽  
Vol 2 (7) ◽  
pp. 18
Author(s):  
R. Jamuna
Keyword(s):  
Data Mining ◽  
Dna Methylation ◽  
Markov Chain ◽  
Human Genome ◽  
Transition Probabilities ◽  
Markov Chain Model ◽  
Effective Means ◽  
Cpg Islands ◽  
Chain Model ◽  
The Given

CpG islands (CGIs) play a vital role in genome analysis as genomic markers.  Identification of the CpG pair has contributed not only to the prediction of promoters but also to the understanding of the epigenetic causes of cancer. In the human genome [1] wherever the dinucleotides CG occurs the C nucleotide (cytosine) undergoes chemical modifications. There is a relatively high probability of this modification that mutates C into a T. For biologically important reasons the mutation modification process is suppressed in short stretches of the genome, such as ‘start’ regions. In these regions [2] predominant CpG dinucleotides are found than elsewhere. Such regions are called CpG islands. DNA methylation is an effective means by which gene expression is silenced. In normal cells, DNA methylation functions to prevent the expression of imprinted and inactive X chromosome genes. In cancerous cells, DNA methylation inactivates tumor-suppressor genes, as well as DNA repair genes, can disrupt cell-cycle regulation. The most current methods for identifying CGIs suffered from various limitations and involved a lot of human interventions. This paper gives an easy searching technique with data mining of Markov Chain in genes. Markov chain model has been applied to study the probability of occurrence of C-G pair in the given   gene sequence. Maximum Likelihood estimators for the transition probabilities for each model and analgously for the  model has been developed and log odds ratio that is calculated estimates the presence or absence of CpG is lands in the given gene which brings in many  facts for the cancer detection in human genome.

Sign in / Sign up

Export Citation Format

Share Document