Guided bone regeneration (GBR) is commonly used in combination with the installment of titanium implants. The application of a membrane to exclude non‐osteogenic tissues from interfering with bone regeneration is a key principle of GBR. Membrane materials possess a number of properties which are amenable to modification. A large number of membranes have been introduced for experimental and clinical verification. This prompts the need for an update on membrane properties and the biological outcomes, as well as a critical assessment of the biological mechanisms governing bone regeneration in defects covered by membranes. The relevant literature for this narrative review was assessed after a MEDLINE/PubMed database search. Experimental data suggest that different modifications of the physicochemical and mechanical properties of membranes may promote bone regeneration. Nevertheless, the precise role of membrane porosities for the barrier function of GBR membranes still awaits elucidation. Novel experimental findings also suggest an active role of the membrane compartment per se in promoting the regenerative processes in the underlying defect during GBR, instead of being purely a passive barrier. The optimization of membrane materials by systematically addressing both the barrier and the bioactive properties is an important strategy in this field of research.

BackgroundBefore considering whether to use a multivariable (diagnostic or prognostic) prediction model, it is essential that its performance be evaluated in data that were not used to develop the model (referred to as external validation). We critically appraised the methodological conduct and reporting of external validation studies of multivariable prediction models.MethodsWe conducted a systematic review of articles describing some form of external validation of one or more multivariable prediction models indexed in PubMed core clinical journals published in 2010. Study data were extracted in duplicate on design, sample size, handling of missing data, reference to the original study developing the prediction models and predictive performance measures.Results11,826 articles were identified and 78 were included for full review, which described the evaluation of 120 prediction models. in participant data that were not used to develop the model. Thirty-three articles described both the development of a prediction model and an evaluation of its performance on a separate dataset, and 45 articles described only the evaluation of an existing published prediction model on another dataset. Fifty-seven percent of the prediction models were presented and evaluated as simplified scoring systems. Sixteen percent of articles failed to report the number of outcome events in the validation datasets. Fifty-four percent of studies made no explicit mention of missing data. Sixty-seven percent did not report evaluating model calibration whilst most studies evaluated model discrimination. It was often unclear whether the reported performance measures were for the full regression model or for the simplified models.ConclusionsThe vast majority of studies describing some form of external validation of a multivariable prediction model were poorly reported with key details frequently not presented. The validation studies were characterised by poor design, inappropriate handling and acknowledgement of missing data and one of the most key performance measures of prediction models i.e. calibration often omitted from the publication. It may therefore not be surprising that an overwhelming majority of developed prediction models are not used in practice, when there is a dearth of well-conducted and clearly reported (external validation) studies describing their performance on independent participant data.

Moderate hypothermia after perinatal asphyxia resulted in improved neurocognitive outcomes in middle childhood. (Funded by the United Kingdom Medical Research Council and others; TOBY ClinicalTrials.gov number, NCT01092637.).

BackgroundThe World Health Organisation estimates that by 2030 there will be approximately 350 million people with type 2 diabetes. Associated with renal complications, heart disease, stroke and peripheral vascular disease, early identification of patients with undiagnosed type 2 diabetes or those at an increased risk of developing type 2 diabetes is an important challenge. We sought to systematically review and critically assess the conduct and reporting of methods used to develop risk prediction models for predicting the risk of having undiagnosed (prevalent) or future risk of developing (incident) type 2 diabetes in adults.MethodsWe conducted a systematic search of PubMed and EMBASE databases to identify studies published before May 2011 that describe the development of models combining two or more variables to predict the risk of prevalent or incident type 2 diabetes. We extracted key information that describes aspects of developing a prediction model including study design, sample size and number of events, outcome definition, risk predictor selection and coding, missing data, model-building strategies and aspects of performance.ResultsThirty-nine studies comprising 43 risk prediction models were included. Seventeen studies (44%) reported the development of models to predict incident type 2 diabetes, whilst 15 studies (38%) described the derivation of models to predict prevalent type 2 diabetes. In nine studies (23%), the number of events per variable was less than ten, whilst in fourteen studies there was insufficient information reported for this measure to be calculated. The number of candidate risk predictors ranged from four to sixty-four, and in seven studies it was unclear how many risk predictors were considered. A method, not recommended to select risk predictors for inclusion in the multivariate model, using statistical significance from univariate screening was carried out in eight studies (21%), whilst the selection procedure was unclear in ten studies (26%). Twenty-one risk prediction models (49%) were developed by categorising all continuous risk predictors. The treatment and handling of missing data were not reported in 16 studies (41%).ConclusionsWe found widespread use of poor methods that could jeopardise model development, including univariate pre-screening of variables, categorisation of continuous risk predictors and poor handling of missing data. The use of poor methods affects the reliability of the prediction model and ultimately compromises the accuracy of the probability estimates of having undiagnosed type 2 diabetes or the predicted risk of developing type 2 diabetes. In addition, many studies were characterised by a generally poor level of reporting, with many key details to objectively judge the usefulness of the models often omitted.

Background and Aims To review the regenerative technologies used in bone regeneration: bone grafts, barrier membranes, bioactive factors and cell therapies. Material and Methods Four background review publications served to elaborate this consensus report. Results and Conclusions Biomaterials used as bone grafts must meet specific requirements: biocompatibility, porosity, osteoconductivity, osteoinductivity, surface properties, biodegradability, mechanical properties, angiogenicity, handling and manufacturing processes. Currently used biomaterials have demonstrated advantages and limitations based on the fulfilment of these requirements. Similarly, membranes for guided bone regeneration (GBR) must fulfil specific properties and potential biological mechanisms to improve their clinical applicability. Pre‐clinical and clinical studies have evaluated the added effect of bone morphogenetic proteins (mainly BMP‐2) and autologous platelet concentrates (APCs) when used as bioactive agents to enhance bone regeneration. Three main approaches using cell therapies to enhance bone regeneration have been evaluated: (a) “minimally manipulated” whole tissue fractions; (b) ex vivo expanded “uncommitted” stem/progenitor cells; and (c) ex vivo expanded “committed” bone‐/periosteum‐derived cells. Based on the evidence from clinical trials, transplantation of cells, most commonly whole bone marrow aspirates (BMA) or bone marrow aspirate concentrations (BMAC), in combination with biomaterial scaffolds has demonstrated an additional effect in sinus augmentation and horizontal ridge augmentation, and comparable bone regeneration to autogenous bone in alveolar cleft repair.