The natural history of relapsing remitting multiple sclerosis (RRMS) is variable and prediction of individual prognosis challenging. The inability to reliably predict prognosis at diagnosis has important implications for informed decision making especially in relation to disease modifying therapies. We conducted a systematic review in order to collate, describe and assess the methodological quality of published prediction models in RRMS. We searched Medline, Embase and Web of Science. Two reviewers independently screened abstracts and full text for eligibility and assessed risk of bias. Studies reporting development or validation of prediction models for RRMS in adults were included. Data collection was guided by the checklist for critical appraisal and data extraction for systematic reviews (CHARMS) and applicability and methodological quality assessment by the prediction model risk of bias assessment tool (PROBAST). 30 studies were included in the review. Applicability was assessed as high risk of concern in 27 studies. Risk of bias was assessed as high for all studies. The single most frequently included predictor was baseline EDSS (n = 11). T2 Lesion volume or number and brain atrophy were each retained in seven studies. Five studies included external validation and none included impact analysis. Although a number of prediction models for RRMS have been reported, most are at high risk of bias and lack external validation and impact analysis, restricting their application to routine clinical practice.

Background Background: Despite falls being an almost universal clinical feature and central to the presentation and diagnostic criteria of progressive supranuclear palsy, our understanding of falls is surprisingly limited and there are few effective treatment options. Objectives Objectives: To provide an overview of the topic of the impact, assessment, mechanism, and management of falls in progressive supranuclear palsy. Methods Methods: We performed a literature search for "falls" and "progressive supranuclear palsy" and included additional relevant literature known to us. We synthesized this literature with experience from clinical practice. ResultsResults: We review current understanding of the pathophysiology of falls, highlighting the roles of the indirect pathway and the pedunculopontine nucleus. We go on to identify shortcomings in commonly used assessments to measure falls. We discuss medical and nonmedical fall prevention strategies, and finally we discuss balancing falls risk against promoting independence. Conclusion Conclusion: Falls are central to progressive supranuclear palsy presentation and diagnosis. Indirect locomotor and pedunculopontine nucleus dysfunction are thought to be the neural substrate of falls in this condition. Attempts to measure and prevent falls, by medical and nonmedical means, are currently limited. A personalized approach is advocated in the management of falls.

Introduction/BackgroundRecent meta-analyses have demonstrated that training in a simple "box" simulator improves minimally invasive surgical skills. 1,2 This is at least as effective as virtual reality simulation, while being much more readily accessible. 3 Box simulator training is optimized when augmented by objective performance feedback, although there is no consensus yet on the best method to provide this. 2,3 A number of techniques have been proposed, including visual tracking using colored markers on the instruments, 4,5 mapping the outline of the instruments against their background, 6,7 radiofrequency identification devices attached to the instrument shafts, 8 electromechanical devices to track movement as the instruments pass through a trocar in the lid of the simulator, 9,10 and "wireless distributive computing" devices attached to instrument handles. 11 While there are exceptions, 4 most of these systems require complex and expensive bespoke hardware and/or 4734S RIXXX10.1177/1553350615594734Surgical InnovationPartridge et al 015

ObjectivesMotor neuron disease (MND) is an incurable progressive neurodegenerative disease with limited treatment options. There is a pressing need for innovation in identifying therapies to take to clinical trial. Here, we detail a systematic and structured evidence-based approach to inform consensus decision making to select the first two drugs for evaluation in Motor Neuron Disease-Systematic Multi-arm Adaptive Randomised Trial (MND-SMART:NCT04302870), an adaptive platform trial. We aim to identify and prioritise candidate drugs which have the best available evidence for efficacy, acceptable safety profiles and are feasible for evaluation within the trial protocol.MethodsWe conducted a two-stage systematic review to identify potential neuroprotective interventions. First, we reviewed clinical studies in MND, Alzheimer’s disease, Huntington’s disease, Parkinson’s disease and multiple sclerosis, identifying drugs described in at least one MND publication or publications in two or more other diseases. We scored and ranked drugs using a metric evaluating safety, efficacy, study size and study quality. In stage two, we reviewed efficacy of drugs in MND animal models, multicellular eukaryotic models and human induced pluripotent stem cell (iPSC) studies. An expert panel reviewed candidate drugs over two shortlisting rounds and a final selection round, considering the systematic review findings, late breaking evidence, mechanistic plausibility, safety, tolerability and feasibility of evaluation in MND-SMART.ResultsFrom the clinical review, we identified 595 interventions. 66 drugs met our drug/disease logic. Of these, 22 drugs with supportive clinical and preclinical evidence were shortlisted at round 1. Seven drugs proceeded to round 2. The panel reached a consensus to evaluate memantine and trazodone as the first two arms of MND-SMART.DiscussionFor future drug selection, we will incorporate automation tools, text-mining and machine learning techniques to the systematic reviews and consider data generated from other domains, including high-throughput phenotypic screening of human iPSCs.