Faculty Bibliography

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.

Objectives/UNASSIGNED:Clinically relevant neuroanatomy is challenging to teach, learn and remember since many functionally important structures are visualized best using histology stains from serial 2D planar sections of the brain. In clinical patients, the locations of specific structures then must be inferred from spatial position and surface anatomy. A 3D MRI dataset of neuroanatomy has several advantages including simultaneous multi-planar visualization in the same brain, direct end-user manipulation of the data and image contrast identical to clinical MRI. We created 3D MRI datasets of the postmortem brain with high spatial and contrast resolution for simultaneous multi-planar visualization of complex neuroanatomy. Materials and Methods/UNASSIGNED:; time = 7 h). Besides resolution, this sequence has multiple adjustments to improve contrast compared to a clinical protocol, including 93% reduced turbo factor and 77% reduced effective echo time. Results/UNASSIGNED:This MRI microscopy protocol provided excellent contrast resolution of small nuclei and internal myelinated pathways within the basal ganglia, thalamus, brainstem, and cerebellum. Contrast was sufficient to visualize the presence and variation of horizontal layers in the cerebral cortex. 3D isotropic resolution datasets facilitated simultaneous multi-planar visualization and efficient production of specific tailored oblique image orientations to improve understanding of complex neuroanatomy. Conclusion/UNASSIGNED:structure visualization.