Systematic review of new medics’ clinical task experience by country

OBJECTIVES: There is a need for research which informs on the overall size and significance of clinical skills deficits among new medics, globally. There is also the need for a meta-review of the similarities and differences between countries in the clinical skills deficits of new medics. DESIGN: A systematic review of published literature produced 68 articles from Google/Scholar, of which 9 met the inclusion criteria (quantitative clinical skills data about new medical doctors). PARTICIPANTS: 1329 new medical doctors (e.g., foundation year-1s, interns, PGY1s). SETTING: Ten countries/regions. MAIN OUTCOME MEASURES: 123 data points and representation of a broad range of clinical procedures. RESULTS: The average rate of inexperience with a wide range of clinical procedures was 35.92% (lower CI 30.84%, upper CI 40.99%). The preliminary meta-analysis showed that the overall deficit in experience is significantly different from 0 in all countries. Focusing on a smaller selection of clinical skills such as catheterisation, IV cannulation, nasogastric tubing and venepuncture, the average rate of inexperience was 26.75% (lower CI 18.55%, upper CI 35.54%) and also significant. England presented the lowest average deficit (9.15%), followed by New Zealand (18.33%), then South Africa (19.53%), Egypt, Kuwait, Gulf Cooperation Council countries and Ireland (21.07%), after which was Nigeria (37.99%), then USA (38.5%), and Iran (44.75%). CONCLUSION: A meta-analysis is needed to include data not yet in the public domain from more countries. These results provide some support for the UK General Medical Council’s clear, detailed curriculum, which has been heralded by other countries as good practice

Mechanisms of intermittent state transitions in a coupled heterogeneous oscillator model of epilepsy

This is the final version of the article. Available from BioMed Central/SpringerOpen via the DOI in this record.We investigate the dynamic mechanisms underlying intermittent state transitions in a recently proposed neural mass model of epilepsy. A low dimensional model is constructed, which preserves two key features of the neural mass model, namely (i) coupling between oscillators and (ii) heterogeneous proximity of these oscillators to a bifurcation between distinct limit cycles. We demonstrate that state transitions due to intermittency occur in the abstract model. This suggests that there is a general bifurcation mechanism responsible for this behaviour and that this is independent of the precise form of the evolution equations. Such abstractions of neural mass models allow a deeper insight into underlying dynamic and physiological mechanisms, and also allow the more efficient exploration of large scale brain dynamics in disease.MG acknowledges funding from the EPSRC through a postdoctoral prize fellowship

A B-Spline-Based Generative Adversarial Network Model for Fast Interactive Airfoil Aerodynamic Optimization

Airfoil aerodynamic optimization is of great importance in aircraft design; however, it relies on high-fidelity physics-based models that are computationally expensive to evaluate. In this work, we provide a methodology to reduce the computational cost for airfoil aerodynamic optimization. Firstly, we develop a B-spline based generative adversarial networks (BSplineGAN) parameterization method to automatically infer design space with sufficient shape variability. Secondly, we construct multi-layer neural network (MNN) surrogates for fast predictions on aerodynamic drag, lift, and pitching moment coefficients. The BSplineGAN has a relative error lower than 1% when fitting to UIUC database. Verification of MNN surrogates shows the root means square errors (RMSE) of all aerodynamic coefficients are within the range of 20%–40% standard deviation of testing points. Both normalized RMSE and relative errors are controlled within 1%. The proposed methodology is then demonstrated on an airfoil aerodynamic optimization. We also verified the baseline and optimized designs using a high-fidelity computational fluid dynamic solver. The proposed framework has the potential to enable web-based fast interactive airfoil aerodynamic optimization

Lessons from Youth in Focus

Youth in Focus (YIF) is a Big Lottery Fund initiative aimed at supporting vulnerable young people through difficult changes in their lives. Beyond Youth Custody (BYC) is one of three England-wide learning and awareness projects funded under the Big Lottery Fund’s YIF programme. BYC has been designed to challenge, advance, and promote better thinking in policy and practice for the effective resettlement of young people after release from custody. BYC brings together Nacro, the social justice charity, with three research and evaluation partners: ARCS (UK), and Salford and Bedfordshire universities, all of which have exceptional track records in action-based research focusing on youth offending and resettlement. The programme was initially funded for a five-year period ending in April 2017. During that period, the partnership delivered a multi-faceted programme of research, networking, publicity and awareness-raising activities. The BYC team produced a wide range of publications and resources for practitioners, policy-makers and researchers. The YIF programme also funded service delivery projects across the country to work with three different client groups: young care-leavers, young carers and young custody-leavers. There were 15 individual YIF projects that worked with young custody-leavers, although some of these projects also worked with care-leavers and young carers. The BYC work focused specifically on young people leaving custody, working alongside these projects and supporting them to evaluate and monitor their own service and compare and contrast different models of resettlement, facilitating young people’s participation and providing ongoing feedback about effective practice and lessons learnt through the research. A key part of BYC’s work involved close and regular involvement with individual YIF projects that worked with young custody-leavers, focusing on issues concerning data collection and evaluation but also on wider practice and policy issues. That involvement with YIF projects generated a substantial set of evidence concerning the implementation and effectiveness of resettlement practice and informed the team’s critical understanding of key resettlement issues

LoANs: Weakly Supervised Object Detection with Localizer Assessor Networks

Recently, deep neural networks have achieved remarkable performance on the task of object detection and recognition. The reason for this success is mainly grounded in the availability of large scale, fully annotated datasets, but the creation of such a dataset is a complicated and costly task. In this paper, we propose a novel method for weakly supervised object detection that simplifies the process of gathering data for training an object detector. We train an ensemble of two models that work together in a student-teacher fashion. Our student (localizer) is a model that learns to localize an object, the teacher (assessor) assesses the quality of the localization and provides feedback to the student. The student uses this feedback to learn how to localize objects and is thus entirely supervised by the teacher, as we are using no labels for training the localizer. In our experiments, we show that our model is very robust to noise and reaches competitive performance compared to a state-of-the-art fully supervised approach. We also show the simplicity of creating a new dataset, based on a few videos (e.g. downloaded from YouTube) and artificially generated data.Comment: To appear in AMV18. Code, datasets and models available at https://github.com/Bartzi/loan

A Model-Based Assessment of the Seizure Onset Zone Predictive Power to Inform the Epileptogenic Zone

This is the final version. Available on open access from Frontiers media via the DOI i this recordEpilepsy surgery is a clinical procedure that aims to remove the brain tissue responsible for the emergence of seizures, the epileptogenic zone (EZ). It is preceded by an evaluation to determine the brain tissue that must be resected. The identification of the seizure onset zone (SOZ) from intracranial EEG recordings stands as one of the key proxies for the EZ. In this study we used computational models of epilepsy to assess to what extent the SOZ may or may not represent the EZ. We considered a set of different synthetic networks (e.g., regular, small-world, random, and scale-free networks) to represent large-scale brain networks and a phenomenological network model of seizure generation. In the model, the SOZ was inferred from the seizure likelihood (SL), a measure of the propensity of single nodes to produce epileptiform dynamics, whilst a surgery corresponded to the removal of nodes and connections from the network. We used the concept of node ictogenicity (NI) to quantify the effectiveness of each node removal on reducing the network's propensity to generate seizures. This framework enabled us to systematically compare the SOZ and the seizure control achieved by each considered surgery. Specifically, we compared the distributions of SL and NI across different networks. We found that SL and NI were concordant when all nodes were similarly ictogenic, whereas when there was a small fraction of nodes with high NI, the SL was not specific at identifying these nodes. We further considered networks with heterogeneous node excitabilities, i.e., nodes with different susceptibilities of being engaged in seizure activity, to understand how such heterogeneity may affect the relationship between SL and NI. We found that while SL and NI are concordant when there is a small fraction of hyper-excitable nodes in a network that is otherwise homogeneous, they do diverge if the network is heterogeneous, such as in scale-free networks. We observe that SL is highly dependent on node excitabilities, whilst the effect of surgical resections as revealed by NI is mostly determined by network structure. Together our results suggest that the SOZ is not always a good marker of the EZ.Medical Research Council (MRC)Epilepsy Research UKEngineering and Physical Sciences Research Council (EPSRC)Wellcome Trus