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Performance at medical school selection correlates with success in Part A of the intercollegiate Membership of the Royal College of Surgeons (MRCS) examination.

Ricky EllisPeter BrennanDuncan Sg ScrimgeourAmanda J LeeJennifer Cleland
Published in: Postgraduate medical journal (2021)
Medical schools in the UK typically use prior academic attainment and an admissions test (University Clinical Aptitude Test (UCAT), Biomedical Admissions Test (BMAT) or the Graduate Medical School Admissions Test (GAMSAT)) to help select applicants for interview. To justify their use, more information is needed about the predictive validity of these tests. Thus, we investigated the relationship between performance in admissions tests and the Membership of the Royal College of Surgeons (MRCS) examination.The UKMED database (https://www.ukmed.ac.uk) was used to access medical school selection data for all UK graduates who attempted MRCS Part A (n=11 570) and Part B (n=5690) between 2007 and 2019. Univariate and multivariate logistic regression models identified independent predictors of MRCS success. Pearson correlation coefficients examined the linear relationship between test scores and MRCS performance.Successful MRCS Part A candidates scored higher in A-Levels, UCAT, BMAT and GAMSAT (p<0.05). No significant differences were observed for MRCS Part B. All admissions tests were found to independently predict MRCS Part A performance after adjusting for prior academic attainment (A-Level performance) (p<0.05). Admission test scores demonstrated statistically significant correlations with MRCS Part A performance (p<0.001).The utility of admissions tests is clear with respect to helping medical schools select from large numbers of applicants for a limited number of places. Additionally, these tests appear to offer incremental value above A-Level performance alone. We expect this data to guide medical schools' use of admissions test scores in their selection process.
Keyphrases
  • healthcare
  • cross sectional
  • data analysis
  • deep learning
  • artificial intelligence
  • thoracic surgery