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Iniencephaly with Craniospinal Rachischisis: A Rare Severe Neural Tube Defect.
J Obstet Gynaecol Can
December 2024
Department of Obstetrics and Gynaecology, All India Institute of Medical Sciences, Bhopal, India.
Key challenges in epidemiology: embracing open science.
J Clin Epidemiol
November 2024
Metaresearch and Open Science Program, University of Ottawa Heart Institute, Ottawa, Ontario, Canada; School of Epidemiology and Public Health, University of Ottawa, Ottawa, Ontario, Canada.
Open science is a movement that fosters research transparency, reproducibility, and equity. Open science has been put forward by numerous stakeholders in the research ecosystem as a key science policy goal, with the United Nations Educational, Scientific, and Cultural Organization creating recommendations on open science and aligning these with UN Sustainability Goals. Open science practices are not standard to epidemiology despite their potential value to the field and especially during disease outbreaks.
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- * The study analyzed over 11,000 sources to assess stillbirth rates across 204 countries from 1990 to 2021, using varied definitions and gestational age benchmarks for clarity.
- * In 2021, the global stillbirth rate was 23.0 per 1,000 births at 20 weeks or longer, with a neonatal mortality rate of 17.1 per 1,000 live births, indicating a concerning level of both stillbirths and neonatal deaths worldwide.
In the era of personalized screening who can benefit more from screening with breast tomosynthesis?
Eur Radiol
January 2025
Diagnostiki Mastou, Larissa, Greece.
Fully Automated Deep Learning Model to Detect Clinically Significant Prostate Cancer at MRI.
Radiology
August 2024
From the Departments of Radiology (J.C.C., H.N., S.K., A.T.F., H.T., P.K., P.R., A.K.B., G.M.C., B.J.E., N.T.) and Urology (L.A.M.), Mayo Clinic, 200 First St SW, Rochester, MN 55905; Department of Radiology, Massachusetts General Hospital, Boston, Mass (J.C.C.); Departments of Radiology (C.W.B.) and Urology (C.D.D.), Mayo Clinic, Jacksonville, Fla; and Departments of Radiology (A.K.) and Urology (M.R.H.), Mayo Clinic, Scottsdale, Ariz.
Background Multiparametric MRI can help identify clinically significant prostate cancer (csPCa) (Gleason score ≥7) but is limited by reader experience and interobserver variability. In contrast, deep learning (DL) produces deterministic outputs. Purpose To develop a DL model to predict the presence of csPCa by using patient-level labels without information about tumor location and to compare its performance with that of radiologists.
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