Abstract
The acceptance of students to a medical school places a considerable emphasis on performance in standardized tests and undergraduate grade point average (uGPA). Traditionally, applicants may be judged as a homogeneous population according to simple quantitative thresholds that implicitly assume a linear relationship between scores and academic success. This ‘one-size-fits-all’ approach ignores the notion that individuals may show distinct patterns of achievement and follow diverse paths to success. In this study, we examined a dataset composed of 53 variables extracted from the admissions application records of 1,088 students matriculating to NYU School of Medicine between the years 2006–2014. We defined training and test groups and applied K-means clustering to search for distinct groups of applicants. Building an optimized logistic regression model, we then tested the predictive value of this clustering for estimating the success of applicants in medical school, aggregating eight performance measures during the subsequent medical school training as a success factor. We found evidence for four distinct clusters of students—we termed ‘signatures’—which differ most substantially according to the absolute level of the applicant’s uGPA and its trajectory over the course of undergraduate education. The ‘risers’ signature showed a relatively higher uGPA and also steeper trajectory; the other signatures showed each remaining combination of these two main factors: ‘improvers’ relatively lower uGPA, steeper trajectory; ‘solids’ higher uGPA, flatter trajectory; ‘statics’ both lower uGPA and flatter trajectory. Examining the success index across signatures, we found that the risers and the statics have significantly higher and lower likelihood of quantifiable success in medical school, respectively. We also found that each signature has a unique set of features that correlate with its success in medical school. The big data approach presented here can more sensitively uncover success potential since it takes into account the inherent heterogeneity within the student population.
Highlights
The medical school admissions process is a resource-intensive challenge for all concerned, with many more applicants than available positions
For each of the four signatures, we could systematically examine the distinguishing features (Fig 2b), according to which, we named the signatures as the ‘statics’, ‘solids’, ‘risers’, and ‘improvers’
The group of students defined as statics (Fig 2a, yellow circles), has significantly more students from top undergraduate schools than expected relative to a randomly selected group of the same size (67.2% vs 52.4%; 95% CI difference [7.7%, 22.1%])
Summary
The medical school admissions process is a resource-intensive challenge for all concerned, with many more applicants than available positions. 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] This ‘one-size-fits-all’ approach, neglects the common sense observation that individuals show distinct patterns of achievement and follow diverse paths to success [14]. We reasoned that instead of studying students as one group, the machine learning derivation of distinct clusters might allow different factors to emerge as being predictive of success To test this idea, we used intensive computational approaches to investigate a large dataset of student records including admissions data to medical school and success in their subsequent education. While this measure of academic success does not necessarily correlate with professional success and patient satisfaction, we sought to elucidate patterns that could be helpful in adjusting the support students receive on their journey through medical education
Sign-in/Register to view highlights & summary Sign-in/Register to access full text options 
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have
Schedule a callDisclaimer: All third-party content on this website/platform is and will remain the property of their respective owners and is provided on "as is" basis without any warranties, express or implied. Use of third-party content does not indicate any affiliation, sponsorship with or endorsement by them. Any references to third-party content is to identify the corresponding services and shall be considered fair use under The CopyrightLaw.
