Incentives, Information, and Emergent Collective Accuracy

Peer Reviewedhttp://deepblue.lib.umich.edu/bitstream/2027.42/93557/1/mde2560.pd

Neoliberalism and the Right Symposium: Introduction

The four articles in this symposium were originally presented as papers at a research workshop on ‘the right and neoliberalism’ held at Queen Mary, University of London, in September 2015. The impetus for the workshop was twofold. First, to reflect on and engage with the avalanche of academic literature and commentary (Gamble, 2009; Mason, 2009; Crouch, 2011; Roubini and Mihm, 2011; Mirowski, 2013) that had emerged in response to the 2008 global financial crisis and, in particular, the question of the ongoing durability and resilience of the neoliberal regime of political economy across the mature capitalist democracies. Secondly, the role of the right and, notably, farright political currents both within neoliberalism and in many of the political responses to the 2008 crisis. Writing this introduction in the wake of the decision by UK voters in June 2016 to depart from the European Union and the election of Donald Trump to the US presidency in November of the same year on a platform defined by nationalist and racist rhetoric and scapegoating reveals all too starkly the connections between neoliberalism and the right that the original workshop was concerned with exploring

Incorporating Cardiac Substructures Into Radiation Therapy For Improved Cardiac Sparing

Growing evidence suggests that radiation therapy (RT) doses to the heart and cardiac substructures (CS) are strongly linked to cardiac toxicities, though only the heart is considered clinically. This work aimed to utilize the superior soft-tissue contrast of magnetic resonance (MR) to segment CS, quantify uncertainties in their position, assess their effect on treatment planning and an MR-guided environment. Automatic substructure segmentation of 12 CS was completed using a novel hybrid MR/computed tomography (CT) atlas method and was improved upon using a 3-dimensional neural network (U-Net) from deep learning. Intra-fraction motion due to respiration was then quantified. The inter-fraction setup uncertainties utilizing a novel MR-linear accelerator were also quantified. Treatment planning comparisons were performed with and without substructure inclusions and methods to reduce radiation dose to sensitive CS were evaluated. Lastly, these described technologies (deep learning U-Net) were translated to an MR-linear accelerator and a segmentation pipeline was created. Automatic segmentations from the hybrid MR/CT atlas was able to generate accurate segmentations for the chambers and great vessels (Dice similarity coefficient (DSC) \u3e 0.75) but coronary artery segmentations were unsuccessful (DSC\u3c0.3). After implementing deep learning, DSC for the chambers and great vessels was ≥0.85 along with an improvement in the coronary arteries (DSC\u3e0.5). Similar accuracy was achieved when implementing deep learning for MR-guided RT. On average, automatic segmentations required ~10 minutes to generate per patient and deep learning only required 14 seconds. The inclusion of CS in the treatment planning process did not yield statistically significant changes in plan complexity, PTV, or OAR dose. Automatic segmentation results from deep learning pose major efficiency and accuracy gains for CS segmentation offering high potential for rapid implementation into radiation therapy planning for improved cardiac sparing. Introducing CS into RT planning for MR-guided RT presented an opportunity for more effective sparing with limited increase in plan complexity

Research design considerations for randomized controlled trials of spinal cord stimulation for pain: IMMPACT/ION/INS recommendations

Spinal cord stimulation (SCS) is an interventional non-pharmacologic treatment used for chronic pain and other indications. Methods for evaluating the safety and efficacy of SCS have evolved from uncontrolled and retrospective studies to prospective randomized controlled trials (RCTs). While randomization overcomes certain types of bias, additional challenges to the validity of RCTs of SCS include blinding, choice of control groups, non-specific effects of treatment variables (e.g., paresthesia, device programming and recharging, psychological support, and rehabilitative techniques), and safety considerations. In order to address these challenges, three professional societies (IMMPACT, ION, INS) convened a meeting to develop consensus recommendations on the design, conduct, analysis, and interpretation of RCTs of SCS for chronic pain. This paper summarizes the results of this meeting. Highlights of our recommendations include disclosing all funding source and potential conflicts; incorporating mechanistic objectives when possible; avoiding non-inferiority designs without internal demonstration of assay sensitivity; achieving and documenting double-blinding whenever possible; documenting investigator and site experience; keeping all information provided to patients balanced with respect to expectation of benefit; disclosing all information provided to patients, including verbal scripts; using placebo/sham controls when possible; capturing a complete set of outcome assessments; accounting for ancillary pharmacologic and non-pharmacologic treatments in a clear manner; providing a complete description of intended and actual programming interactions; making a prospective ascertainment of SCS-specific safety outcomes; training patients and researchers on appropriate expectations, outcome assessments, and other key aspects of study performance; and providing transparent and complete reporting of results according to applicable reporting guidelines

Cortisol changes secondary to chiropractic treatment in asthma sufferers

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