2009 CKD-EPI glomerular filtration rate estimation in Black individuals outside the United States: a systematic review and meta-analysis.

Background: The 2009 Chronic Kidney Disease Epidemiology Collaboration (CKD-EPI) equation is the most used equation to estimate glomerular filtration rate (GFR), with race being a factor thereof, increasing GFR by 16% in self-identified Black persons compared with non-Black persons. However, recent publications indicate that it might overestimate GFR for Black adults outside the USA. In this meta-analysis, we assessed the accuracy, evaluated by the percentage of estimated GFR within 30% of measured GFR (P30), of the 2009 CKD-EPI equation in estimating GFR with and without the race coefficient in Black individuals outside the United States of America (USA). Methods: We searched MEDLINE and Embase from inception to 9 July 2022, with no language restriction, supplemented by manual reference searches. Studies that assessed the CKD-EPI P30 accuracy with or without the race coefficient in Black adults outside the USA with an adequate method of GFR measurement were included. Data were extracted by independent pairs of reviewers and were pooled using a random-effects model. Results: We included 11 studies, with a total of 1834 Black adults from South America, Africa and Europe. The race coefficient in the 2009 CKD-EPI equation significantly decreased P30 accuracy {61.9% [95% confidence interval (CI) 53-70%] versus 72.9% [95% CI 66.7-78.3%]; P = .03}. Conclusions: Outside the USA, the 2009 CKD-EPI equation should not be used with the race coefficient, even though the 2009 CKD-EPI equation is not sufficiently accurate either way (<75%). Thus we endorse the Kidney Disease: Improving Global Outcomes guidelines to use exogenous filtration markers when this may impact clinical conduct.

Accuracy of doppler assessment of the uterine arteries for the diagnosis of pubertal onset in girls: a scoping review.

The uterine artery pulsatility index (PI) assessed by Doppler ultrasound reflects the impedance to the blood flow in the vessel distal to the sampling point. We aimed to assess the accuracy of the uterine artery PI for the diagnosis of puberty in girls. A PRISMA-ScR-compliant scoping review was performed in the MEDLINE and Embase databases with the search terms "puberty" and "Doppler ultrasonography". Studies that included girls aged 0-18 years who underwent pelvic Doppler ultrasound with calculation of uterine artery PI were eligible. Ten studies comprising 1385 girls aged 1.2-18 years were included. The selected studies included participants from Italy, Brazil, Iran, Belgium and Denmark, and were published between 1996 and 2021. Six studies selected girls who were referred for evaluation of pubertal disorders, while four studies included only healthy girls. Nine studies found a significant difference in Doppler signal pattern and PI according to pubertal stage, with PI cutoff points ranging from 2.5 to 4.6 for the diagnosis of puberty, with a sensitivity of 77%-94%, specificity of 85%-100%, and accuracy of 79%-98%. Doppler assessment of the uterine arteries with PI calculation is a useful noninvasive tool in the diagnosis of pubertal onset in girls.

Switching to online: Testing the validity of supervised remote testing for online reinforcement learning experiments.

Online experiments are an alternative for researchers interested in conducting behavioral research outside the laboratory. However, an online assessment might become a challenge when long and complex experiments need to be conducted in a specific order or with supervision from a researcher. The aim of this study was to test the computational validity and the feasibility of a remote and synchronous reinforcement learning (RL) experiment conducted during the social-distancing measures imposed by the pandemic. An additional feature of this study was to describe how a behavioral experiment originally created to be conducted in-person was transformed into an online supervised remote experiment. Open-source software was used to collect data, conduct statistical analysis, and do computational modeling. Python codes were created to replicate computational models that simulate the effect of working memory (WM) load over RL performance. Our behavioral results indicated that we were able to replicate remotely and with a modified behavioral task the effects of working memory (WM) load over RL performance observed in previous studies with in-person assessments. Our computational analyses using Python code also captured the effects of WM load over RL as expected, which suggests that the algorithms and optimization methods were reliable in their ability to reproduce behavior. The behavioral and computational validation shown in this study and the detailed description of the supervised remote testing may be useful for researchers interested in conducting long and complex experiments online.