CNS Maturation Process in Children and Adolescents in the Northern Region of the Russian Federation and Its Reflection in the Dynamics of Integral EEG Parameters

Neuroscience and Behavioral Physiology - We present here results from analysis of the formation of brain bioelectrical activity in children and adolescents living in the northern region of Russia...     

Ex vivo porcine model for eye,eyelid, and orbit movement analysis of 4-mm ferromagnetic foreign bodies in MRI

Graefe's Archive for Clinical and Experimental Ophthalmology - Ferromagnetic foreign bodies (FFB) present during magnetic resonance imaging (MRI) explorations can lead to tissue injury due to...     

Evaluating Head Models for Cortical Source Localization of the Face-Sensitive N290 Component in Infants

Brain Topography - Accurate cortical source localization of event-related potentials (ERPs) requires using realistic head models constructed from the participant’s structural magnetic...     

PREDICT-GTN 1: Can we improve the FIGO scoring system in gestational trophoblastic neoplasia?

Gestational trophoblastic neoplasia (GTN) patients are treated according to the eight-variable International Federation of Gynaecology and Obstetrics (FIGO) scoring system, that aims to predict first-line single-agent chemotherapy resistance. FIGO is imperfect with one-third of low-risk patients developing disease resistance to first-line single-agent chemotherapy. We aimed to generate simplified models that improve upon FIGO. Logistic regression (LR) and multilayer perceptron (MLP) modelling (n = 4191) generated six models (M1-6). M1, all eight FIGO variables (scored data); M2, all eight FIGO variables (scored and raw data); M3, nonimaging variables (scored data); M4, nonimaging variables (scored and raw data); M5, imaging variables (scored data); and M6, pretreatment hCG (raw data) + imaging variables (scored data). Performance was compared to FIGO using true and false positive rates, positive and negative predictive values, diagnostic odds ratio, receiver operating characteristic (ROC) curves, Bland-Altman calibration plots, decision curve analysis and contingency tables. M1-6 were calibrated and outperformed FIGO on true positive rate and positive predictive value. Using LR and MLP, M1, M2 and M4 generated small improvements to the ROC curve and decision curve analysis. M3, M5 and M6 matched FIGO or performed less well. Compared to FIGO, most (excluding LR M4 and MLP M5) had significant discordance in patient classification (McNemar's test P < .05); 55-112 undertreated, 46-206 overtreated. Statistical modelling yielded only small gains over FIGO performance, arising through recategorisation of treatment-resistant patients, with a significant proportion of under/overtreatment as the available data have been used a priori to allocate primary chemotherapy. Streamlining FIGO should now be the focus.     

Correction to: “Living with COVID”—implications for immunosuppressed and immunocompromised

Clinical Rheumatology -