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Background: There is a widely held assumption that research engagement improves health-care performance at various levels, but little direct empirical evidence. Objectives: To conduct a theoretically and empirically grounded synthesis to map and explore plausible mechanisms through which research engagement might improve health services performance. A review of the effects on patients of their health-care practitioner's or institution's participation in clinical trials was published after submission of the proposal for this review. It identified only 13 relevant papers and, overall, suggested that the evidence that research engagement improves health-care performance was less strong than some thought. We aimed to meet the need for a wider review. Methods: An hourglass review was developed, consisting of three stages: (1) a planning and mapping stage; (2) a focused review concentrating on the core question of whether or not research engagement improves health care; and (3) a wider (but less systematic) review of papers identified during the two earlier stages. Studies were included in the focused review if the concept of ‘engagement in research’ was an input and some measure of ‘performance’ an output. The search strategy covered the period 1990 to March 2012. MEDLINE, EMBASE, PsycINFO, Cumulative Index to Nursing and Allied Health Literature (CINAHL), Web of Science and other relevant databases were searched. A total of 10,239 papers were identified through the database searches, and 159 from other sources. A further relevance and quality check on 473 papers was undertaken, and identified 33 papers for inclusion in the review. A standard meta-analysis was not possible on the heterogeneous mix of papers in the focused review. Therefore an explanatory matrix was developed to help characterise the circumstances in which research engagement might improve health-care performance and the mechanisms that might be at work, identifying two main dimensions along which to categorise the studies: the degree of intentionality and the scope of the impact. Results: Of the 33 papers in the focused review, 28 were positive (of which six were positive/mixed) in relation to the question of whether or not research engagement improves health-care performance. Five papers were negative (of which two were negative/mixed). Seven out of 28 positive papers reported some improvement in health outcomes. For the rest, the improved care took the form of improved processes of care. Nine positive papers were at a clinician level and 19 at an institutional level. The wider review demonstrated, for example, how collaborative and action research can encourage some progress along the pathway from research engagement towards improved health-care performance. There is also evidence that organisations in which the research function is fully integrated into the organisational structure out-perform other organisations that pay less formal heed to research and its outputs. The focused and wider reviews identified the diversity in the mechanisms through which research engagement might improve health care: there are many circumstances and mechanisms at work, more than one mechanism is often operative, and the evidence available for each one is limited. Limitations: To address the complexities of this evidence synthesis of research we needed to spend significant time mapping the literature, and narrowed the research question to make it feasible. We excluded many potentially relevant papers (though we partially addressed this by conducting a wider additional synthesis). Studies assessing the impact made on clinician behaviour by small, locally conducted pieces of research could be difficult to interpret without full knowledge of the context. Conclusions: Drawing on the focused and wider reviews, it is suggested that when clinicians and health-care organisations engage in research there is the likelihood of a positive impact on health-care performance. Organisations that have deliberately integrated the research function into organisational structures demonstrate how research engagement can, among other factors, contribute to improved health-care performance. Further explorations are required of research networks and schemes to promote the engagement of clinicians and managers in research. Detailed observational research focusing on research engagement within organisations would build up an understanding of mechanisms. Study registration: PROSPERO: CRD42012001990. Funding: The National Institute for Health Research Health Services and Delivery Research programme.

Abstract: Multimedia learning materials are now available in many academic disciplines. With their interactive and dynamic approach they are potentially a means of improving the quality of student learning. They address students' learning needs in a variety of ways and offer students involvement in and control over their learning process, even in classes that are larger than traditional tutorial groups. Effective use of these new materials requires, however, that they be integrated with the rest of the course and that students have appropriate support and guidance in their use. The teaching process is defined by Laurillard (1993) as forming a link between the world and the learner. Not only do we need good educational materials but we also need to use them in a way that enables students to learn from them. This paper shows how different kinds of courseware features help various individuals achieve their learning goals and how a Workbook can complement software, ensuring students get the maximum benefit from the screens. A combination of materials that offer flexibility in use can meet a variety of different learning needs. It can also incorporate a number of different teaching strategies that are recognised as contributing to the advance of learning.The examples shown are taken from the WinEcon economics courseware and an accompanying WinEcon Workbook. WinEcon evaluation results are given, together with information on how the courseware is at present being used. Reviewers: Edith Esch (Cambridge U.), Greg Kearsley (George Washington U.), Agnes Kukulska-Hulme (Open U.) Interactive elements: 'The WinEcon interactive sampler presents a substantial amount of interactive material, and the author's audio-visual slideshow introduces some of the key elements of WinEcon's design.' Fully interactive 'sampler' demonstrations of WinEcon ('Student' and 'Lecturer' samplers) can be downloaded from the WinEcon website (samplers require Microsoft Windows 3.1/95/98). The author also describes key features of WinEcon in an Audio-visual slideshow. UPDATE: A Web/Java version of WinEcon called Web.Econ is now in development.

Under the TLTP initiative, the Economics Consortium is developing an interactive computerbased learning package called WinEcon. The package is directed at first-year economics undergraduates, particularly those taking economics as a supplementary course. Using recent technological developments, the aim is both to facilitate a further increase in student numbers without a proportionate increase in teaching staff, and to provide a better method of student learning. Some key elements of WinEcon are set out in this paper and demonstrated by screens produced at Leicester University. Methods of presenting textual information that give the user control over accessing it are described. For learning difficult concepts, a visual active learning approach is discussed. It involves user interaction and step-by-step analysis. The importance of flexibility and choice is emphasized, and the capacity of the computer to assist in deepening and consolidating learning is shown.

Background: Humanized mice transplanted with CD34 + hematopoietic cells (HPCs) are broadly used to study human immune responses and infections in vivo and for testing therapies pre-clinically. However, until now, it was not clear whether interactions between the mouse major histocompatibility complexes (MHCs) and/or the human leukocyte antigens (HLAs) were necessary for human T-cell development and immune reactivity., Methods: We evaluated the long-term (20-week) human hematopoiesis and human T-cell development in NOD Scid Gamma (NSG) mice lacking the expression of MHC class I and II (NSG-DKO). Triplicate experiments were performed with HPCs obtained from three donors, and humanization was confirmed in the reference strain NOD Rag Gamma (NRG). Further, we tested whether humanized NSG-DKO mice would respond to a lentiviral vector (LV) systemic delivery of HLA-A*02:01, HLA-DRB1*04:01, human GM-CSF/IFN-α, and the human cytomegalovirus gB antigen., Results: Human immune reconstitution was detectable in peripheral blood from 8 to 20 weeks after the transplantation of NSG-DKO. Human single positive CD4 + and CD8 + T-cells were detectable in lymphatic tissues (thymus, bone marrow, and spleen). LV delivery harnessed the detection of lymphocyte subsets in bone marrow (αβ and γδ T-cells and NK cells) and the expression of HLA-DR. Furthermore, RNA sequencing showed that LV delivery increased the expression of different human reactome pathways, such as defense responses to other organisms and viruses., Conclusions: Human T-cell development and reactivity are independent of the expression of murine MHCs in humanized mice. Therefore, humanized NSG-DKO is a promising new model for studying human immune responses, as it abrogates the xenograft mouse MHC interference.

Background: There is an often-held assumption that the engagement of clinicians and healthcare organizations in research improves healthcare performance at various levels. Previous reviews found up to 28 studies suggesting a positive association between the engagement of individuals and healthcare organizations in research and improvements in healthcare performance. The current study sought to provide an update., Methods: We updated our existing published systematic review by again addressing the question: Does research engagement (by clinicians and organizations) improve healthcare performance? The search covered the period 1 January 2012 to March 2024, in two phases. First, the formal updated search ran from 1 January 2012 to 31 May 2020, in any healthcare setting or country and focussed on English language publications. In this phase two searches identified 66 901 records. Later, a further check of key journals and citations to identified papers ran from May 2020 to March 2024. In total, 168 papers progressed to full-text appraisal; 62 were identified for inclusion in the update. Then we combined papers from our original and updated reviews., Results: In the combined review, the literature is dominated by papers from the United States (50/95) and mostly drawn from the Global North. Papers cover various clinical fields, with more on cancer than any other field; 86 of the 95 papers report positive results, of which 70 are purely positive and 16 positive/mixed, meaning there are some negative elements (i.e. aspects where there is a lack of healthcare improvement) in their findings., Conclusions: The updated review collates a substantial pool of studies, especially when combined with our original review, which are largely positive in terms of the impact of research engagement on processes of care and patient outcomes. Of the potential engagement mechanisms, the review highlights the important role played by research networks. The review also identifies various papers which consider how far there is a "dose effect" from differing amounts of research engagement. Additional lessons come from analyses of equity issues and negative papers. This review provides further evidence of contributions played by systems level research investments such as research networks on processes of care and patient outcomes., (© 2024. The Author(s).)

Aspirin is a non-steroidal, anti-inflammatory drug used for a range of indications. For patients with aspirin hypersensitivities, a desensitization procedure may be prescribed, and the initial low doses of <81 mg need to be provided by compounded preparations. Compounding with aspirin is associated with stability challenges due to its poor chemical stability. Additionally, low-strength preparations often exhibit dosage accuracy and uniformity issues. This study was designed to assess the feasibility of compounding low-strength aspirin capsules for the use in desensitization protocols. Aspirin capsules of 40-mg, 10-mg, 3-mg, and 1-mg strengths were prepared by manual filling of dry powders. Formulations were kept as simple as possible for ease of compounding, and the ingredients and compounding procedures were carefully selected to minimize the moisture content and to optimize the dosage accuracy. For the 40-mg and 10-mg capsules, two formulations were tested, using pure drug or crushed tablet powder. For the 3-mg and 1-mg capsules, only one formulation was tested, using a 5% mixture of pure drug and cellulose. All formulations were filled into hydroxypropyl methylcellulose capsule shells and stored at room temperature for 90 days. A  stability indicating, high-performance liquid chromatography method was used to analyze the quality of the capsules. The initial potency results of all capsule formulations were within 100% to 105% of the label claim, and the standard deviation was <3% for all formulations except the 1-mg strength (7%). The use of crushed tablet powder over pure drug powder appeared to reduce the potency variability, probably due to the larger fill weight per capsule. Upon storage at room temperature, the 40-mg and 10-mg formulations retained >90% of the label claim for up to 90 days, but the 3-mg and 1-mg formulations retained >90% of the label claim for up to only 31 days. Low-strength aspirin capsules were prepared successfully by compounding with a beyond-use date of at least 31 days at room temperature. However, the overall trend confirmed the challenges of achieving dosage uniformity and aspirin stability at 3-mg and 1-mg strengths. For general application in compounding pharmacies, trial batches are recommended with proper analytical testing., (Copyright© by International Journal of Pharmaceutical Compounding, Inc.)

Objective: To assemble and characterize an electronic health record (EHR) dataset for a large cohort of US military Veterans diagnosed with ALS (Amyotrophic Lateral Sclerosis). Methods: An EHR dataset for 19,662 Veterans diagnosed with ALS between January 1, 2000 to December 31, 2020 was compiled from the Veterans Health Administration (VHA) EHR database by a query for ICD9 diagnosis (335.20) or ICD10 diagnosis (G12.21) for Amyotrophic Lateral Sclerosis. Results: The cohort is predominantly male (98.94%) and white (72.37%) with a median age at disease onset of 68 years and median survival from the date of diagnosis of 590 days. With the designation of ALS as a compensable illness in 2009, there was a subsequent increase in the number of Veterans diagnosed per year in the VHA, but no change in median survival. The cohort included a greater-than-expected proportion of individuals whose branch of service at the time of separation was the Army. Conclusions: The composition of the cohort reflects the VHA population who are at greatest risk for ALS. The greater than expected proportion of individuals whose branch of service at the time of separation was the Army suggests the possibility of a branch-specific risk factor for ALS.

Cancer is the leading cause of death worldwide. Cancer immunotherapy involves reinvigorating the patient's own immune system to fight against cancer. While novel approaches like Chimeric Antigen Receptor (CAR) T cells, bispecific T cell engagers, and immune checkpoint inhibitors have shown promising efficacy, Cytokine Release Syndrome (CRS) is a serious adverse effect and remains a major concern. CRS is a phenomenon of immune hyperactivation that results in excessive cytokine secretion, and if left unchecked, it may lead to multi-organ failure and death. Here we review the pathophysiology of CRS, its occurrence and management in the context of cancer immunotherapy, and the screening approaches that can be used to assess CRS and de-risk drug discovery earlier in the clinical setting with more predictive pre-clinical data. Furthermore, the review also sheds light on the potential immunotherapeutic approaches that can be used to overcome CRS associated with T cell activation., Competing Interests: The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest., (Copyright © 2023 Shah, Soper and Shopland.)

Continuous-time hidden Markov models are an attractive approach for disease modeling because they are explainable and capable of handling both irregularly sampled, skewed and sparse data arising from real-world medical practice, in particular to screening data with extensive followup. Most applications in this context consider time-homogeneous models due to their relative computational simplicity. However, the time homogeneous assumption is too strong to accurately model the natural history of many diseases including cancer. Moreover, cancer risk across the population is not homogeneous either, since exposure to disease risk factors can vary considerably between individuals. This is important when analyzing longitudinal datasets and different birth cohorts. We model the heterogeneity of disease progression and regression using piece-wise constant intensity functions and model the heterogeneity of risks in the population using a latent mixture structure. Different submodels under the mixture structure employ the same types of Markov states reflecting disease progression and allowing both clinical interpretation and model parsimony. We also consider flexible observational models dealing with model over-dispersion in real data. An efficient, scalable Expectation-Maximization algorithm for inference is proposed with the theoretical guaranteed convergence property. We demonstrate our method's superior performance compared to other state-of-the-art methods using synthetic data and a real-world cervical cancer screening dataset from the Cancer Registry of Norway. Moreover, we present two model-based risk stratification methods that identify the risk levels of individuals.

Background: The interaction between cancer diagnoses and COVID-19 infection and outcomes is unclear. We leveraged a state-wide, multi-institutional database to assess cancer-related risk factors for poor COVID-19 outcomes., Methods: We conducted a retrospective cohort study using the University of California Health COVID Research Dataset, which includes electronic health data of patients tested for severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) at 17 California medical centers. We identified adults tested for SARS-CoV-2 from 2/1/2020-12/31/2020 and selected a cohort of patients with cancer. We obtained demographic, clinical, cancer type, and antineoplastic therapy data. The primary outcome was hospitalization within 30d after the first positive SARS-CoV-2 test. Secondary outcomes were SARS-CoV-2 positivity and severe COVID-19 (intensive care, mechanical ventilation, or death within 30d after the first positive test). We used multivariable logistic regression to identify cancer-related factors associated with outcomes., Results: We identified 409,462 patients undergoing SARS-CoV-2 testing. Of 49,918 patients with cancer, 1781 (3.6%) tested positive. Patients with cancer were less likely to test positive (RR 0.70, 95% CI: 0.67-0.74, p < 0.001). Among the 1781 SARS-CoV-2-positive patients with cancer, BCR/ABL-negative myeloproliferative neoplasms (RR 2.15, 95% CI: 1.25-3.41, p = 0.007), venetoclax (RR 2.96, 95% CI: 1.14-5.66, p = 0.028), and methotrexate (RR 2.72, 95% CI: 1.10-5.19, p = 0.032) were associated with greater hospitalization risk. Cancer and therapy types were not associated with severe COVID-19., Conclusions: In this large, diverse cohort, cancer was associated with a decreased risk of SARS-CoV-2 positivity. Patients with BCR/ABL-negative myeloproliferative neoplasm or receiving methotrexate or venetoclax may be at increased risk of hospitalization following SARS-CoV-2 infection. Mechanistic and comparative studies are needed to validate findings., (© 2022 The Authors. Cancer Medicine published by John Wiley & Sons Ltd.)

The combination of machine learning (ML) and electronic health records (EHR) data may be able to improve outcomes of hospitalized COVID-19 patients through improved risk stratification and patient outcome prediction. However, in resource constrained environments the clinical utility of such data-driven predictive tools may be limited by the cost or unavailability of certain laboratory tests. We leveraged EHR data to develop an ML-based tool for predicting adverse outcomes that optimizes clinical utility under a given cost structure. We further gained insights into the decision-making process of the ML models through an explainable AI tool. This cohort study was performed using deidentified EHR data from COVID-19 patients from ProMedica Health System in northwest Ohio and southeastern Michigan. We tested the performance of various ML approaches for predicting either increasing ventilatory support or mortality. We performed post hoc analysis to obtain optimal feature sets under various budget constraints. We demonstrate that it is possible to achieve a significant reduction in cost at the expense of a small reduction in predictive performance. For example, when predicting ventilation, it is possible to achieve a 43% reduction in cost with only a 3% reduction in performance. Similarly, when predicting mortality, it is possible to achieve a 50% reduction in cost with only a 1% reduction in performance. This study presents a quick, accurate, and cost-effective method to evaluate risk of deterioration for patients with SARS-CoV-2 infection at the time of clinical evaluation., (© 2021. The Author(s).)

Advances in the modeling and analysis of electronic health records (EHR) have the potential to improve patient risk stratification, leading to better patient outcomes. The modeling of complex temporal relations across the multiple clinical variables inherent in EHR data is largely unexplored. Existing approaches to modeling EHR data often lack the flexibility to handle time-varying correlations across multiple clinical variables, or they are too complex for clinical interpretation. Therefore, we propose a novel nonstationary multivariate Gaussian process model for EHR data to address the aforementioned drawbacks of existing methodologies. Our proposed model is able to capture time-varying scale, correlation and smoothness across multiple clinical variables. We also provide details on two inference approaches: Maximum a posteriori and Hamilton Monte Carlo. Our model is validated on synthetic data and then we demonstrate its effectiveness on EHR data from Kaiser Permanente Division of Research (KPDOR). Finally, we use the KPDOR EHR data to investigate the relationships between a clinical patient risk metric and the latent processes of our proposed model and demonstrate statistically significant correlations between these entities., (Copyright © 2021 Elsevier Inc. All rights reserved.)

Oncology is a highly siloed field of research in which sub-disciplinary specialization has limited the amount of information shared between researchers of distinct cancer types. This can be attributed to legitimate differences in the physiology and carcinogenesis of cancers affecting distinct anatomical sites. However, underlying processes that are shared across seemingly disparate cancers probably affect prognosis. The objective of the current study is to investigate whether multitask learning improves 5-year survival cancer patient survival prediction by leveraging information across anatomically distinct HPV related cancers. Data were obtained from the Surveillance, Epidemiology, and End Results (SEER) program database. The study cohort consisted of 29,768 primary cancer cases diagnosed in the United States between 2004 and 2015. Ten different cancer diagnoses were selected, all with a known association with HPV risk. In the analysis, the cancer diagnoses were categorized into three distinct topography groups of varying specificity. The most specific topography grouping consisted of 10 original cancer diagnoses differentiated by the first two digits of the ICD-O-3 topography code. The second topography grouping consisted of cancer diagnoses categorized into six distinct organ groups. Finally, the third topography grouping consisted of just two groups, head-neck cancers and ano-genital cancers. The tasks were to predict 5-year survival for patients within the different topography groups using 14 predictive features which were selected among descriptive variables available in the SEER database. The information from the predictive features was shared between tasks in three different ways, resulting in three distinct predictive models: 1) Information was not shared between patients assigned to different tasks (single task learning); 2) Information was shared between all patients, regardless of task (pooled model); 3) Only relevant information was shared between patients grouped to different tasks (multitask learning). Prediction performance was evaluated with Brier scores. All three models were evaluated against one another on each of the three distinct topography-defined tasks. The results showed that multitask classifiers achieved relative improvement for the majority of the scenarios studied compared to single task learning and pooled baseline methods. In this study, we have demonstrated that sharing information among anatomically distinct cancer types can lead to improved predictive survival models., Competing Interests: The authors have declared that no competing interests exist.

Mice xenotransplanted with human cells and/or expressing human gene products (also known as "humanized mice") recapitulate the human evolutionary specialization and diversity of genotypic and phenotypic traits. These models can provide a relevant in vivo context for understanding of human-specific physiology and pathologies. Humanized mice have advanced toward mainstream preclinical models and are now at the forefront of biomedical research. Here, we considered innovations and challenges regarding the reconstitution of human immunity and human tissues, modeling of human infections and cancer, and the use of humanized mice for testing drugs or regenerative therapy products. As the number of publications exploring different facets of humanized mouse models has steadily increased in past years, it is becoming evident that standardized reporting is needed in the field. Therefore, an international community-driven resource called "Minimal Information for Standardization of Humanized Mice" (MISHUM) has been created for the purpose of enhancing rigor and reproducibility of studies in the field. Within MISHUM, we propose comprehensive guidelines for reporting critical information generated using humanized mice., (© 2020 The Authors. Published under the terms of the CC BY 4.0 license.)

Background: Machine learning (ML) has made a significant impact in medicine and cancer research; however, its impact in these areas has been undeniably slower and more limited than in other application domains. A major reason for this has been the lack of availability of patient data to the broader ML research community, in large part due to patient privacy protection concerns. High-quality, realistic, synthetic datasets can be leveraged to accelerate methodological developments in medicine. By and large, medical data is high dimensional and often categorical. These characteristics pose multiple modeling challenges., Methods: In this paper, we evaluate three classes of synthetic data generation approaches; probabilistic models, classification-based imputation models, and generative adversarial neural networks. Metrics for evaluating the quality of the generated synthetic datasets are presented and discussed., Results: While the results and discussions are broadly applicable to medical data, for demonstration purposes we generate synthetic datasets for cancer based on the publicly available cancer registry data from the Surveillance Epidemiology and End Results (SEER) program. Specifically, our cohort consists of breast, respiratory, and non-solid cancer cases diagnosed between 2010 and 2015, which includes over 360,000 individual cases., Conclusions: We discuss the trade-offs of the different methods and metrics, providing guidance on considerations for the generation and usage of medical synthetic data.

Multitask learning (MTL) leverages commonalities across related tasks with the aim of improving individual task performance. A key modeling choice in designing MTL models is the structure of the tasks' relatedness, which may not be known. Here we propose a Bayesian multitask learning model that is able to infer the task relationship structure directly from the data. We present two variations of the model in terms of a priori information of task relatedness. First, a diffuse Wishart prior is placed on a task precision matrix so that all tasks are assumed to be equally related a priori. Second, a Bayesian graphical LASSO prior is used on the task precision matrix to impose sparsity in the task relatedness. Motivated by machine learning applications in the biomedical domain, we emphasize interpretability and uncertainty quantification in our models. To encourage model interpretability, linear mappings from the shared input spaces to task-dependent output spaces are used. To encourage uncertainty quantification, conjugate priors are used so that full posterior inference is possible. Using synthetic data, we show that our model is able to recover the underlying task relationships as well as features jointly relevant for all tasks. We demonstrate the utility of our model on three distinct biomedical applications: Alzheimer's disease progression, Parkinson's disease assessment, and cervical cancer screening compliance. We show that our model outperforms Single Task (STL) models in terms of predictive performance, and performs better than existing MTL methods for the majority of the scenarios., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2019 The Authors. Published by Elsevier Inc. All rights reserved.)

Objective: There is a widely held assumption that engagement by clinicians and healthcare organisations in research improves healthcare performance at various levels, but little direct empirical evidence has previously been collated. The objective of this study was to address the question: Does research engagement (by clinicians and organisations) improve healthcare performance?, Methods: An hourglass-shaped review was developed, consisting of three stages: (1) a planning and mapping stage; (2) a focused review concentrating on the core question of whether or not research engagement improves healthcare performance; and (3) a wider (but less systematic) review of papers identified during the two earlier stages, focusing on mechanisms., Results: Of the 33 papers included in the focused review, 28 identified improvements in health services performance. Seven out of these papers reported some improvement in health outcomes, with others reporting improved processes of care. The wider review demonstrated that mechanisms such as collaborative and action research can encourage some progress along the pathway from research engagement towards improved healthcare performance. Organisations that have deliberately integrated the research function into organisational structures demonstrate how research engagement can, among other factors, contribute to improved healthcare performance., Conclusions: Current evidence suggests that there is an association between the engagement of individuals and healthcare organisations in research and improvements in healthcare performance. The mechanisms through which research engagement might improve healthcare performance overlap and rarely act in isolation, and their effectiveness often depends on the context in which they operate., (Published by the BMJ Publishing Group Limited. For permission to use (where not already granted under a licence) please go to http://www.bmj.com/company/products-services/rights-and-licensing/)

Objectives: The nine NIHR CLAHRCs are collaborations between universities and local NHS organizations that seek to improve patient outcomes through the conduct and application of applied health research. The theoretical and practical context within which the CLAHRCs were set up was characterized by a considerable degree of uncertainty, and the CLAHRCs were established as a natural experiment., Methods: We adopted a formative and emergent evaluation approach. Drawing on in-depth, multi-method case studies of two CLAHRCs we explored how they pursued their remit by supporting efforts to increase the relevance and use of health research, and building relationships., Results: Both CLAHRCs: strengthened local networks and relationships; built capacity in their local academic and NHS communities to undertake and use research that meets the needs of the service; developed research and implementation methodologies; and added to understanding of the complex relation between research and implementation. There was evidence of impact of CLAHRC projects on health and social care services. Informed by the literature on implementing collaborative research initiatives, knowledge transfer and exchange and cultural change, some key lessons can be drawn., Conclusion: The CLAHRCs pursued a strategy that can be categorized as one of flexible comprehensiveness; i.e. their programmes have been flexible and responsive and they have used a range of approaches that seek to match the diverse aspects of the complex issues they face. Key features include their work on combining a range of knowledge transfer and exchange strategies, their efforts to promote cultural change, and the freedom to experiment, learn and adapt. Although the CLAHRCs do not, by themselves, have the remit or resources to bring about wholesale service improvement in health care, they do have features that would allow them to play a key role in some of the wider initiatives that encourage innovation.

This article describes a review of the Biomedical Research Units (BRU) scheme, undertaken for the Department of Health. This review was a perceptions audit of senior executives involved in the scheme, and explored what impact they felt the scheme is having on the translational research landscape. More specifically, we investigated whether and how institutional relationships between NHS and academic partners, industry and other health research system players are changing because of the scheme; how the scheme is helping build critical mass in specific priority disease areas; and the effects of any changes on efforts to deliver the broader goals set out in Best Research for Best Health. The views presented are those of study informants only. The information obtained through our interviews suggests that the BRU scheme is significantly helping shape the health research system to pursue translational research and innovation, with the clear goal of realising patient benefit. The BRUs are already contributing to observable changes in institutional relationships between the NHS and academic partners: trusts and medical schools are collaborating more closely than in the past, have signed up to the same vision of translational research from bench to bedside, and are managing and governing targeted research resources more professionally and transparently than in the past. There is also a stronger emphasis on engaging industry and more strategic thinking about strengthening regional and national collaboration with other hospital trusts, PCTs, research organisations, networks and development agencies. The scheme is also transforming capacity building in the health research system. This includes (i) developing and modernising facilities and equipment for translation; (ii) building a critical mass of human resources through recruitment and training, as well as improving retention of existing expertise; and (iii) helping ensure a steady flow of funds needed to sustain research activity and accelerate movement through the innovation pipeline. A number of centres are also trying to recreate the BRU model in new disease areas, with their own resources.

Health research systems consist of diverse groups who have some role in health research, but the boundaries around such a system are not clear-cut. To explore what various stakeholders need we reviewed the literature including that on the history of English health R&D reforms, and we also applied some relevant conceptual frameworks.We first describe the needs and capabilities of the main groups of stakeholders in health research systems, and explain key features of policymaking systems within which these stakeholders operate in the UK. The five groups are policymakers (and health care managers), health professionals, patients and the general public, industry, and researchers. As individuals and as organisations they have a range of needs from the health research system, but should also develop specific capabilities in order to contribute effectively to the system and benefit from it.Second, we discuss key phases of reform in the development of the English health research system over four decades - especially that of the English Department of Health's R&D system - and identify how far legitimate demands of key stakeholder interests were addressed.Third, in drawing lessons we highlight points emerging from contemporary reports, but also attempt to identify issues through application of relevant conceptual frameworks. The main lessons are: the importance of comprehensively addressing the diverse needs of various interacting institutions and stakeholders; the desirability of developing facilitating mechanisms at interfaces between the health research system and its various stakeholders; and the importance of additional money in being able to expand the scope of the health research system whilst maintaining support for basic science.We conclude that the latest health R&D strategy in England builds on recent progress and tackles acknowledged weaknesses. The strategy goes a considerable way to identifying and more effectively meeting the needs of key groups such as medical academics, patients and industry, and has been remarkably successful in increasing the funding for health research. There are still areas that might benefit from further recognition and resourcing, but the lessons identified, and progress made by the reforms are relevant for the design and coordination of national health research systems beyond England.

Non-invasive event-related potential (ERP) recordings have become a popular technique to study neural activity associated with saccades in humans. To date, it is not known whether nonhuman primates exhibit similar saccade-related ERPs. Here, we recorded ERPs associated with the performance of randomly interleaved pro- and anti-saccades in macaque monkeys. Stimulus-aligned ERPs showed short-latency visual component with more negative P2 and N2 peak amplitudes on anti- than on pro-saccade trials. Saccade-aligned ERPs showed a larger presaccadic negativity on anti- than pro-saccade trials, and a presaccadic positivity on pro-saccade trials, which was attenuated or absent on anti-saccade trials. This was followed by sharp negative spike potential immediately prior to the movement. Overall, these findings demonstrate that macaque monkeys, like humans, exhibit task-related differences of visual ERPs associated with pro- and anti-saccades and furthermore share presaccadic positivity as well as a spike potential prior to these tasks. We suggest that the presaccadic positivity on pro-saccade trials is generated by a source in the contralateral frontal eye fields and that the more negative voltage on anti-saccade trials is the result of additional sources of opposite polarity in neighboring frontal areas.

In 2004 a UK charity, The Health Foundation, established the 'Engaging with Quality Initiative' to explore and evaluate the benefits of engaging clinicians in quality improvement in healthcare. Eight projects run by professional bodies or specialist societies were commissioned in various areas of acute care. A developmental approach to the initiative was adopted, accompanied by a two level evaluation: eight project self-evaluations and a related external evaluation. This paper describes how the protocol for the external evaluation was developed. The challenges faced included large variation between and within the projects (in approach, scope and context, and in understanding of quality improvement), the need to support the project teams in their self-evaluations while retaining a necessary objectivity, and the difficulty of evaluating the moving target created by the developmental approach adopted in the initiative. An initial period to develop the evaluation protocol proved invaluable in helping us to explore these issues.