Historically, admissions committees for biomedical Ph.D. programs have heavily weighed GRE scores when considering applications for admission. The predictive validity of GRE scores on graduate student success is unclear, and there have been no recent investigations specifically on the relationship between general GRE scores and graduate student success in biomedical research. Data from Vanderbilt University Medical School's biomedical umbrella program were used to test to what extent GRE scores can predict outcomes in graduate school training when controlling for other admissions information. Overall, the GRE did not prove useful in predicating who will graduate with a Ph.D., pass the qualifying exam, have a shorter time to defense, deliver more conference presentations, publish more first author papers, or obtain an individual grant or fellowship. GRE scores were found to be moderate predictors of first semester grades, and weak to moderate predictors of graduate GPA and some elements of a faculty evaluation. These findings suggest admissions committees of biomedical doctoral programs should consider minimizing their reliance on GRE scores to predict the important measures of progress in the program and student productivity.
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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC5226333 | PMC |
http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0166742 | PLOS |
Sci Rep
December 2024
GIN, IMN-UMR5293, CEA, CNRS, Université de Bordeaux, Bordeaux, France.
Cerebral microbleeds (CMB) represent a feature of cerebral small vessel disease (cSVD), a prominent vascular contributor to age-related cognitive decline, dementia, and stroke. They are visible as spherical hypointense signals on T2*- or susceptibility-weighted magnetic resonance imaging (MRI) sequences. An increasing number of automated CMB detection methods being proposed are based on supervised deep learning (DL).
View Article and Find Full Text PDFStroke Vasc Interv Neurol
November 2024
NIH/NINDS, Stroke Branch, Bethesda, MD, USA.
Background: The characterization of hemorrhage following acute stroke intervention has largely been CT-based. We sought to compare MRI- and CT-based scoring of hemorrhage after acute endovascular therapy (EVT) applying the Heidelberg Bleeding Classification (HBC) to assess inter-modal agreement and quantify inter-rater agreement.
Methods: Consecutive acute stroke patients were included in this retrospective study if they: i) had MRI and CT ≤12 hours of each other OR ii) had CT bracketed by MRI pre- and post-CT [i.
J Allied Health
December 2024
Dep. of Physical Therapy, Midwestern University, 555 31st Street, Alumni Hall 340A, Downers Grove, IL 60515, USA. Tel 630-515-6359.
AJNR Am J Neuroradiol
November 2024
From the Department of Neuroradiology (M.G.-M., N.C., V.A.K., K.S., M.C., M.W.), Department of Biostatistics (J.P.L.), Department of Imaging Physics (J.S., P.H., H.-L.L.), Department of Radiation Oncology (C.C., C.W.), Division of Diagnostic Imaging (E.S.), and Department of Neurosurgery (J.W.), The University of Texas MD Anderson Cancer Center, Houston, Texas, USA.
AJNR Am J Neuroradiol
November 2024
Department of Radiology and Research Institute of Radiology (P.S.S., C.H.S., H.H., W.H.S., H.S.K., S.J.K.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Radiology and Research Institute of Radiological Science and Center for Clinical Imaging Data Science(P.S.S.), Yonsei University College of Medicine, Seoul, South Korea; Heuron Co.Ltd. (H.H., M.O.L., S.H.S., D.H.S.) Seoul, Republic of Korea; Department of Neurology (S.J.C., S.Y.J.), Asan Medical Center, University of Ulsan College of Medicine, Seoul, Republic of Korea; Department of Radiology (E.Y.K), Samsung Medical Center, Sungkyunkwan University School of Medicine, Seoul, Republic of Korea.
Background And Purpose: To develop and validate a deep learning-based automatic quantification for nigral hyperintensity and a classification algorithm for neurodegenerative parkinsonism using susceptibility map-weighted imaging (SMwI).
Materials And Methods: We retrospectively collected 450 participants (210 with idiopathic Parkinson's disease [IPD] and 240 individuals in the control group) for training data between November 2022 and May 2023, and 237 participants (168 with IPD, 58 with essential tremor, and 11 with drug-induced Parkinsonism) for validation data between July 2021 and January 2022. SMwI data were reconstructed from multi-echo GRE.
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