The characteristics of patients with possible Transient Ischemic Attack and Minor Stroke in the Hunter and Manning Valley regions, Australia (the INSIST Study)

This is the final version. Available on open access from the American Academy of Neurology via the DOI in this record. Background: Transient ischemic attack (TIA) and minor stroke (TIAMS) are risk factors for stroke recurrence. Some TIAMS may be preventable by appropriate primary prevention. We aimed to recruit “possible-TIAMS” patients in the INternational comparison of Systems of care and patient outcomes In minor Stroke and TIA (INSIST) study. Methods: A prospective inception cohort study performed across 16 Hunter–Manning region, Australia, general practices in the catchment of one secondary-care acute neurovascular clinic. Possible-TIAMS patients were recruited from August 2012 to August 2016. We describe the baseline demographics, risk factors and pre-event medications of participating patients. Results: There were 613 participants (mean age; 69 ± 12 years, 335 women), and 604 (99%) were Caucasian. Hypertension was the most common risk factor (69%) followed by hyperlipidemia (52%), diabetes mellitus (17%), atrial fibrillation (AF) (17%), prior TIA (13%) or stroke (10%). Eighty-nine (36%) of the 249 participants taking antiplatelet therapy had no known history of cardiovascular morbidity. Of 102 participants with known AF, 91 (89%) had a CHA2DS2-VASc score ≥ 2 but only 47 (46%) were taking anticoagulation therapy. Among 304 participants taking an antiplatelet or anticoagulant agent, 30 (10%) had stopped taking these in the month prior to the index event. Conclusion: This study provides the first contemporary data on TIAMS or TIAMS-mimics in Australia. Community and health provider education is required to address the under-use of anticoagulation therapy in patients with known AF, possibly inappropriate use of antiplatelet therapy and possibly inappropriate discontinuation of antiplatelet or anticoagulation therapy.National Health and Medical Research Counci

The contribution of cause-effect link to representing the core of scientific paper—The role of Semantic Link Network

The Semantic Link Network is a general semantic model for modeling the structure and the evolution of complex systems. Various semantic links play different roles in rendering the semantics of complex system. One of the basic semantic links represents cause-effect relation, which plays an important role in representation and understanding. This paper verifies the role of the Semantic Link Network in representing the core of text by investigating the contribution of cause-effect link to representing the core of scientific papers. Research carries out with the following steps: (1) Two propositions on the contribution of cause-effect link in rendering the core of paper are proposed and verified through a statistical survey, which shows that the sentences on cause-effect links cover about 65% of key words within each paper on average. (2) An algorithm based on syntactic patterns is designed for automatically extracting cause-effect link from scientific papers, which recalls about 70% of manually annotated cause-effect links on average, indicating that the result adapts to the scale of data sets. (3) The effects of cause-effect link on four schemes of incorporating cause-effect link into the existing instances of the Semantic Link Network for enhancing the summarization of scientific papers are investigated. The experiments show that the quality of the summaries is significantly improved, which verifies the role of semantic links. The significance of this research lies in two aspects: (1) it verifies that the Semantic Link Network connects the important concepts to render the core of text; and, (2) it provides an evidence for realizing content services such as summarization, recommendation and question answering based on the Semantic Link Network, and it can inspire relevant research on content computing

Practice change in chronic conditions care: an appraisal of theories

Open Access This article is distributed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits unrestricted use, distribution, and reproduction in any medium, provided you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license, and indicate if changes were made. The Creative Commons Public Domain Dedication waiver (http://creativecommons.org/publicdomain/zero/1.0/) applies to the data made available in this article, unless otherwise stated.Background Management of chronic conditions can be complex and burdensome for patients and complex and costly for health systems. Outcomes could be improved and costs reduced if proven clinical interventions were better implemented, but the complexity of chronic care services appears to make clinical change particularly challenging. Explicit use of theories may improve the success of clinical change in this area of care provision. Whilst theories to support implementation of practice change are apparent in the broad healthcare arena, the most applicable theories for the complexities of practice change in chronic care have not yet been identified. Methods We developed criteria to review the usefulness of change implementation theories for informing chronic care management and applied them to an existing list of theories used more widely in healthcare. Results Criteria related to the following characteristics of chronic care: breadth of the field; multi-disciplinarity; micro, meso and macro program levels; need for field-specific research on implementation requirements; and need for measurement. Six theories met the criteria to the greatest extent: the Consolidate Framework for Implementation Research; Normalization Process Theory and its extension General Theory of Implementation; two versions of the Promoting Action on Research Implementation in Health Services framework and Sticky Knowledge. None fully met all criteria. Involvement of several care provision organizations and groups, involvement of patients and carers, and policy level change are not well covered by most theories. However, adaptation may be possible to include multiple groups including patients and carers, and separate theories may be needed on policy change. Ways of qualitatively assessing theory constructs are available but quantitative measures are currently partial and under development for all theories. Conclusions Theoretical bases are available to structure clinical change research in chronic condition care. Theories will however need to be adapted and supplemented to account for the particular features of care in this field, particularly in relation to involvement of multiple organizations and groups, including patients, and in relation to policy influence. Quantitative measurement of theory constructs may present difficulties

A prediction rule to stratify mortality risk of patients with pulmonary tuberculosis

Tuberculosis imposes high human and economic tolls, including in Europe. This study was conducted to develop a severity assessment tool for stratifying mortality risk in pulmonary tuberculosis (PTB) patients. A derivation cohort of 681 PTB cases was retrospectively reviewed to generate a model based on multiple logistic regression analysis of prognostic variables with 6-month mortality as the outcome measure. A clinical scoring system was developed and tested against a validation cohort of 103 patients. Five risk features were selected for the prediction model: hypoxemic respiratory failure (OR 4.7, 95% CI 2.8-7.9), age >= 50 years (OR 2.9, 95% CI 1.7-4.8), bilateral lung involvement (OR 2.5, 95% CI 1.44.4), >= 1 significant comorbidity-HIV infection, diabetes mellitus, liver failure or cirrhosis, congestive heart failure and chronic respiratory disease-(OR 2.3, 95% CI 1.3-3.8), and hemoglobin = 6) mortality risk. The mortality associated with each group was 2.9%, 22.9% and 53.9%, respectively. The model performed equally well in the validation cohort. We provide a new, easy-to-use clinical scoring system to identify PTB patients with high-mortality risk in settings with good healthcare access, helping clinicians to decide which patients are in need of closer medical care during treatment.This work was supported by Fundacao Amelia de Mello/Jose de Mello Saude and Sociedade Portuguesa de Pneumologia (SPP). This work was developed under the scope of the project NORTE-01-0145-FEDER-000013, supported by the Northern Portugal Regional Operational Programme (NORTE 2020), under the Portugal 2020 Partnership Agreement, through the European Regional Development Fund (FEDER). NSO is a FCT (Fundacao para a Ciencia e Tecnologia) investigator. MS is an Associate FCT Investigator. The fundershad no role in study design, data collection and analysis, decision to publish, or preparation of the manuscript

Efficient Physical Embedding of Topologically Complex Information Processing Networks in Brains and Computer Circuits

Nervous systems are information processing networks that evolved by natural selection, whereas very large scale integrated (VLSI) computer circuits have evolved by commercially driven technology development. Here we follow historic intuition that all physical information processing systems will share key organizational properties, such as modularity, that generally confer adaptivity of function. It has long been observed that modular VLSI circuits demonstrate an isometric scaling relationship between the number of processing elements and the number of connections, known as Rent's rule, which is related to the dimensionality of the circuit's interconnect topology and its logical capacity. We show that human brain structural networks, and the nervous system of the nematode C. elegans, also obey Rent's rule, and exhibit some degree of hierarchical modularity. We further show that the estimated Rent exponent of human brain networks, derived from MRI data, can explain the allometric scaling relations between gray and white matter volumes across a wide range of mammalian species, again suggesting that these principles of nervous system design are highly conserved. For each of these fractal modular networks, the dimensionality of the interconnect topology was greater than the 2 or 3 Euclidean dimensions of the space in which it was embedded. This relatively high complexity entailed extra cost in physical wiring: although all networks were economically or cost-efficiently wired they did not strictly minimize wiring costs. Artificial and biological information processing systems both may evolve to optimize a trade-off between physical cost and topological complexity, resulting in the emergence of homologous principles of economical, fractal and modular design across many different kinds of nervous and computational networks