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Signatures of medical student applicants and academic success

Author

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  • Tal Baron
  • Robert I Grossman
  • Steven B Abramson
  • Martin V Pusic
  • Rafael Rivera
  • Marc M Triola
  • Itai Yanai

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.

Suggested Citation

  • Tal Baron & Robert I Grossman & Steven B Abramson & Martin V Pusic & Rafael Rivera & Marc M Triola & Itai Yanai, 2020. "Signatures of medical student applicants and academic success," PLOS ONE, Public Library of Science, vol. 15(1), pages 1-13, January.
  • Handle: RePEc:plo:pone00:0227108
    DOI: 10.1371/journal.pone.0227108
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    References listed on IDEAS

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    1. Liane Moneta-Koehler & Abigail M Brown & Kimberly A Petrie & Brent J Evans & Roger Chalkley, 2017. "The Limitations of the GRE in Predicting Success in Biomedical Graduate School," PLOS ONE, Public Library of Science, vol. 12(1), pages 1-17, January.
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    1. Rola Khamisy-Farah & Peter Gilbey & Leonardo B. Furstenau & Michele Kremer Sott & Raymond Farah & Maurizio Viviani & Maurizio Bisogni & Jude Dzevela Kong & Rosagemma Ciliberti & Nicola Luigi Bragazzi, 2021. "Big Data for Biomedical Education with a Focus on the COVID-19 Era: An Integrative Review of the Literature," IJERPH, MDPI, vol. 18(17), pages 1-16, August.

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