Trajectories of Emotion Recognition Training in Virtual Reality and Predictors of Improvement for People with a Psychotic Disorder.

Meta-analyses have found that social cognition training (SCT) has large effects on the emotion recognition ability of people with a psychotic disorder. Virtual reality (VR) could be a promising tool for delivering SCT. Presently, it is unknown how improvements in emotion recognition develop during (VR-)SCT, which factors impact improvement, and how improvements in VR relate to improvement outside VR.

Systematic review finds "spin" practices and poor reporting standards in studies on machine learning-based prediction models.

Objectives: We evaluated the presence and frequency of spin practices and poor reporting standards in studies that developed and/or validated clinical prediction models using supervised machine learning techniques.

Study Design And Setting: We systematically searched PubMed from 01/2018 to 12/2019 to identify diagnostic and prognostic prediction model studies using supervised machine learning. No restrictions were placed on data source, outcome, or clinical specialty.

Systematic review identifies the design and methodological conduct of studies on machine learning-based prediction models.

Background And Objectives: We sought to summarize the study design, modelling strategies, and performance measures reported in studies on clinical prediction models developed using machine learning techniques.

Methods: We search PubMed for articles published between 01/01/2018 and 31/12/2019, describing the development or the development with external validation of a multivariable prediction model using any supervised machine learning technique. No restrictions were made based on study design, data source, or predicted patient-related health outcomes.

Dynamic Interactive Social Cognition Training in Virtual Reality (DiSCoVR) versus Virtual Reality Relaxation (VRelax) for People With a Psychotic Disorder: A Single-Blind Multicenter Randomized Controlled Trial.

Background And Hypothesis: Social cognition training (SCT), an intervention for social cognition and social functioning, might be improved by using virtual reality (VR), because VR may offer better opportunities to practice in a potentially more realistic environment. To date, no controlled studies have investigated VR-SCT. This study investigated a VR-SCT, "DiSCoVR".

Home monitoring of lung function, symptoms and quality of life after admission with COVID-19 infection: The HOMECOMIN' study.

Background And Objective: To develop targeted and efficient follow-up programmes for patients hospitalized with coronavirus disease 2019 (COVID-19), structured and detailed insights in recovery trajectory are required. We aimed to gain detailed insights in long-term recovery after COVID-19 infection, using an online home monitoring programme including home spirometry. Moreover, we evaluated patient experiences with the home monitoring programme.

Completeness of reporting of clinical prediction models developed using supervised machine learning: a systematic review.

Background: While many studies have consistently found incomplete reporting of regression-based prediction model studies, evidence is lacking for machine learning-based prediction model studies. We aim to systematically review the adherence of Machine Learning (ML)-based prediction model studies to the Transparent Reporting of a multivariable prediction model for Individual Prognosis Or Diagnosis (TRIPOD) Statement.

Methods: We included articles reporting on development or external validation of a multivariable prediction model (either diagnostic or prognostic) developed using supervised ML for individualized predictions across all medical fields.

Guidelines and quality criteria for artificial intelligence-based prediction models in healthcare: a scoping review.

While the opportunities of ML and AI in healthcare are promising, the growth of complex data-driven prediction models requires careful quality and applicability assessment before they are applied and disseminated in daily practice. This scoping review aimed to identify actionable guidance for those closely involved in AI-based prediction model (AIPM) development, evaluation and implementation including software engineers, data scientists, and healthcare professionals and to identify potential gaps in this guidance. We performed a scoping review of the relevant literature providing guidance or quality criteria regarding the development, evaluation, and implementation of AIPMs using a comprehensive multi-stage screening strategy.

The P5-type ATPase ATP13A1 modulates major histocompatibility complex I-related protein 1 (MR1)-mediated antigen presentation.

The monomorphic antigen-presenting molecule major histocompatibility complex-I-related protein 1 (MR1) presents small-molecule metabolites to mucosal-associated invariant T (MAIT) cells. The MR1-MAIT cell axis has been implicated in a variety of infectious and noncommunicable diseases, and recent studies have begun to develop an understanding of the molecular mechanisms underlying this specialized antigen presentation pathway. However, proteins regulating MR1 folding, loading, stability, and surface expression remain to be identified.

Missing data is poorly handled and reported in prediction model studies using machine learning: a literature review.

Objectives: Missing data is a common problem during the development, evaluation, and implementation of prediction models. Although machine learning (ML) methods are often said to be capable of circumventing missing data, it is unclear how these methods are used in medical research. We aim to find out if and how well prediction model studies using machine learning report on their handling of missing data.

Risk of bias in studies on prediction models developed using supervised machine learning techniques: systematic review.

Objective: To assess the methodological quality of studies on prediction models developed using machine learning techniques across all medical specialties.

Design: Systematic review.

Data Sources: PubMed from 1 January 2018 to 31 December 2019.

The Novel Nucleoside Analogue ProTide NUC-7738 Overcomes Cancer Resistance Mechanisms and in a First-In-Human Phase I Clinical Trial.

Purpose: Nucleoside analogues form the backbone of many therapeutic regimens in oncology and require the presence of intracellular enzymes for their activation. A ProTide is comprised of a nucleoside fused to a protective phosphoramidate cap. ProTides are easily incorporated into cells whereupon the cap is cleaved and a preactivated nucleoside released.

Virtual reality facial emotion recognition in social environments: An eye-tracking study.

Background: Virtual reality (VR) enables the administration of realistic and dynamic stimuli within a social context for the assessment and training of emotion recognition. We tested a novel VR emotion recognition task by comparing emotion recognition across a VR, video and photo task, investigating covariates of recognition and exploring visual attention in VR.

Methods: Healthy individuals (n = 100) completed three emotion recognition tasks; a photo, video and VR task.

[Early hospital discharge for patients with COVID-19: evaluation of the transmural healthcare program in Amsterdam].

Objective: Evaluation of an early discharge program for COVID-19-patients who still required additional oxygen support, supervised by their own general practitioner (GP) in a home setting. We evaluated safety and gathered experiences from patients, caregivers and GPs.

Design: Cohort study (prospective and retrospective inclusion) METHOD: Adult COVID-19-patients admitted to one of the three Amsterdam hospitals, the Netherlands, were eligible when clinically stable for at least 48 hours, with a minimum oxygen saturation of 94% and a maximum of 3 l/min oxygen support.

Bicycle Helmets and Bicycle-Related Traumatic Brain Injury in the Netherlands.

The aim of this study was to determine the association between bicycle helmet use in adults (16 years and older) and traumatic brain injury (TBI) in emergency departments (EDs) in the Netherlands.The conducted research was a retrospective case-control study in patients aged 16 years and older who sustained a bicycle accident and therefore visited the EDs of participating hospitals throughout 2016. Cases were patients with TBI; controls were patients without TBI but with other trauma.

Internal-external cross-validation helped to evaluate the generalizability of prediction models in large clustered datasets.

Objective: To illustrate how to evaluate the need of complex strategies for developing generalizable prediction models in large clustered datasets.

Study Design And Setting: We developed eight Cox regression models to estimate the risk of heart failure using a large population-level dataset. These models differed in the number of predictors, the functional form of the predictor effects (non-linear effects and interaction) and the estimation method (maximum likelihood and penalization).

Real-time imputation of missing predictor values improved the application of prediction models in daily practice.

Objectives: In clinical practice, many prediction models cannot be used when predictor values are missing. We, therefore, propose and evaluate methods for real-time imputation.

Study Design And Setting: We describe (i) mean imputation (where missing values are replaced by the sample mean), (ii) joint modeling imputation (JMI, where we use a multivariate normal approximation to generate patient-specific imputations), and (iii) conditional modeling imputation (CMI, where a multivariable imputation model is derived for each predictor from a population).

Real-time imputation of missing predictor values in clinical practice.

Aims: Use of prediction models is widely recommended by clinical guidelines, but usually requires complete information on all predictors, which is not always available in daily practice. We aim to describe two methods for real-time handling of missing predictor values when using prediction models in practice.

Methods And Results: We compare the widely used method of mean imputation (M-imp) to a method that personalizes the imputations by taking advantage of the observed patient characteristics.

Protocol for a systematic review on the methodological and reporting quality of prediction model studies using machine learning techniques.

Introduction: Studies addressing the development and/or validation of diagnostic and prognostic prediction models are abundant in most clinical domains. Systematic reviews have shown that the methodological and reporting quality of prediction model studies is suboptimal. Due to the increasing availability of larger, routinely collected and complex medical data, and the rising application of Artificial Intelligence (AI) or machine learning (ML) techniques, the number of prediction model studies is expected to increase even further.

Dynamic Interactive Social Cognition Training in Virtual Reality (DiSCoVR) for People With a Psychotic Disorder: Single-Group Feasibility and Acceptability Study.

Background: People with a psychotic disorder commonly experience problems in social cognition and functioning. Social cognition training (SCT) improves social cognition, but may inadequately simulate real-life social interactions. Virtual reality (VR) provides a realistic, interactive, customizable, and controllable training environment, which could facilitate the application of skills in daily life.