Uses and Gratifications of Initiating Use of Gamifed Learning Platforms

Research on gamified educational platforms has chiefly focused on game elements motivating continued engagement, neglecting whether and why people choose to use them in the first place. Grounded in Uses & Gratifications Theory, this study therefore combined use diaries with follow-up interviews to explore the situated reasons for use of 83 students who voluntarily used a gamified online learning platform. Partial data analysis suggested a motivational threshold of gamification: game design elements don’t motivate the initiation of new use sessions per se, but are able to prolong an already started session. Some other pre- existing sought uses and gratifications are required for gamification to work, although gamification may indirectly support these. Main reasons for initiating use of a gamified learning platform were learning, curiosity, fun, need for closure, and competence

A process for designing algorithm-based personalized gamification

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Collecting Pokémon or receiving rewards? : How people functionalise badges in gamified online learning environments in the wild

Do game design elements like badges have one, fixed motivational effect or can they have several different? Self-Determination Theory suggests that people situationally appraise the functional significance or psychological meaning of a given stimulus, which can result in different motivational states, but there is little empirical work observing actual functionalisations of game design elements. We therefore conducted a qualitative in-the-wild diary and interview study with 81 university students who reported on their experiences with badges on two popular gamified online learning platforms, Khan Academy and Codecademy. Participants functionalised badges in nine distinct ways that only partially align with prior theory. Functionalisations shaped experience and motivation and prompted function-aligned behaviour. Badge design details fostered but did not determine different functionalisations, while no user or context characteristics were identified that reliably linked to particular functionalisations. We conclude that future research may need to conceptualise game design elements in a more differentiated way to capture what aspects support different motivational functions

Observational Dutch Young Symptomatic StrokE studY (ODYSSEY): Study rationale and protocol of a multicentre prospective cohort study

Background: The proportion of strokes occurring in younger adults has been rising over the past decade. Due to the far longer life expectancy in the young, stroke in this group has an even larger socio-economic impact. However, information on etiology and prognosis remains scarce.Methods/design: ODYSSEY is a multicentre prospective cohort study on the prognosis and risk factors of patients with a first-ever TIA, ischemic stroke or intracerebral hemorrhage aged 18 to 49 years. Our aim is to include 1500 patients. Primary outcome will be all cause mortality and risk of recurrent vascular events. Secondary outcome will be the risk of post-stroke epilepsy and cognitive impairment. Patients will complete structured questionnaires on outcome measures and risk factors. Both well-documented and less well-documented risk factors and potentially acute trigger factors will be investigated. Patients will be followed every 6 months for at least 3 years. In addition, an extensive neuropsychological assessment will be administered both at baseline and 1 year after the stroke/TIA. Furthermore we will include 250 stroke-free controls, who will complete baseline assessment and one neuropsychological assessment.Discussion: ODYSSEY is designed to prospectively determine prognosis after a young stroke and get more insight into etiology of patients with a TIA, ischemic stroke and intracerebral hemorrhage in patients aged 18 to 49 years old in a large sample size

Effect of Sleep Apnea and Continuous Positive Airway Pressure on Cardiac Structure and Recurrence of Atrial Fibrillation

Background: Sleep apnea (SA) is associated with an increased risk of atrial fibrillation (AF). We sought to determine the effect of SA on cardiac structure in patients with AF, whether therapy for SA was associated with beneficial cardiac structural remodelling, and whether beneficial cardiac structural remodelling translated into a reduced risk of recurrence of AF after pulmonary venous isolation (PVI). Methods and Results: A consecutive group of 720 patients underwent a cardiac magnetic resonance study before PVI. Patients with SA (n=142, 20%) were more likely to be male, diabetic, and hypertensive and have an increased pulmonary artery pressure, right ventricular volume, atrial dimensions, and left ventricular mass. Treated SA was defined as duration of continuous positive airway pressure therapy of >4 hours per night. Treated SA patients (n=71, 50%) were more likely to have paroxysmal AF, a lower blood pressure, lower ventricular mass, and smaller left atrium. During a follow‐up of 42 months, AF recurred in 245 patients. The cumulative incidence of AF recurrence was 51% in patients with SA, 30% in patients without SA, 68% in patients with untreated SA, and 35% in patients with treated SA. In a multivariable model, the presence of SA (hazard ratio 2.79, CI 1.97 to 3.94, P<0.0001) and untreated SA (hazard ratio 1.61, CI 1.35 to 1.92, P<0.0001) were highly associated with AF recurrence. Conclusions: Patients with SA have an increased blood pressure, pulmonary artery pressure, right ventricular volume, left atrial size, and left ventricular mass. Therapy with continuous positive airway pressure is associated with lower blood pressure, atrial size, and ventricular mass, and a lower risk of AF recurrence after PVI

Cost impact of procalcitonin-guided decision making on duration of antibiotic therapy for suspected early-onset sepsis in neonates

Abstract Backgrounds The large, international, randomized controlled NeoPInS trial showed that procalcitonin (PCT)-guided decision making was superior to standard care in reducing the duration of antibiotic therapy and hospitalization in neonates suspected of early-onset sepsis (EOS), without increased adverse events. This study aimed to perform a cost-minimization study of the NeoPInS trial, comparing health care costs of standard care and PCT-guided decision making based on the NeoPInS algorithm, and to analyze subgroups based on country, risk category and gestational age. Methods Data from the NeoPInS trial in neonates born after 34 weeks of gestational age with suspected EOS in the first 72 h of life requiring antibiotic therapy were used. We performed a cost-minimization study of health care costs, comparing standard care to PCT-guided decision making. Results In total, 1489 neonates were included in the study, of which 754 were treated according to PCT-guided decision making and 735 received standard care. Mean health care costs of PCT-guided decision making were not significantly different from costs of standard care (€3649 vs. €3616). Considering subgroups, we found a significant reduction in health care costs of PCT-guided decision making for risk category ‘infection unlikely’ and for gestational age ≥ 37 weeks in the Netherlands, Switzerland and the Czech Republic, and for gestational age < 37 weeks in the Czech Republic. Conclusions Health care costs of PCT-guided decision making of term and late-preterm neonates with suspected EOS are not significantly different from costs of standard care. Significant cost reduction was found for risk category ‘infection unlikely,’ and is affected by both the price of PCT-testing and (prolonged) hospitalization due to SAEs

Machine learning used to compare the diagnostic accuracy of risk factors, clinical signs and biomarkers and to develop a new prediction model for neonatal early-onset sepsis

Background: Current strategies for risk stratification and prediction of neonatal early-onset sepsis (EOS) are inefficient and lack diagnostic performance. The aim of this study was to use machine learning to analyze the diagnostic accuracy of risk factors (RFs), clinical signs and biomarkers and to develop a prediction model for culture-proven EOS. We hypothesized that the contribution to diagnostic accuracy of biomarkers is higher than of RFs or clinical signs. Study Design: Secondary analysis of the prospective international multicenter NeoPInS study. Neonates born after completed 34 weeks of gestation with antibiotic therapy due to suspected EOS within the first 72 hours of life participated. Primary outcome was defined as predictive performance for culture-proven EOS with variables known at the start of antibiotic therapy. Machine learning was used in form of a random forest classifier. Results: One thousand six hundred eighty-five neonates treated for suspected infection were analyzed. Biomarkers were superior to clinical signs and RFs for prediction of culture-proven EOS. C-reactive protein and white blood cells were most important for the prediction of the culture result. Our full model achieved an area-under-the-receiver-operating-characteristic-curve of 83.41% (±8.8%) and an area-under-the-precision-recall-curve of 28.42% (±11.5%). The predictive performance of the model with RFs alone was comparable with random. Conclusions: Biomarkers have to be considered in algorithms for the management of neonates suspected of EOS. A 2-step approach with a screening tool for all neonates in combination with our model in the preselected population with an increased risk for EOS may have the potential to reduce the start of unnecessary antibiotics

Randomised controlled trial of a psychiatric consultation model for treatment of common mental disorder in the occupational health setting

BACKGROUND: Common mental disorders are the most prevalent of all mental disorders, with the highest burden in terms of work absenteeism and utilization of health care services. Evidence-based treatments are available, but recognition and treatment could be improved, especially in the occupational health setting. The situation in this setting has recently changed in the Netherlands because of new legislation, which has resulted in reduced sickness absence. Severe mental disorder has now become one of the main causes of work absenteeism. Occupational physicians (OPs) are expected to take an active role in diagnosis and treatment, and seem to be in need of support for a new approach to handle cases of more complex mental disorders. Psychiatric consultation can be a collaborative care model to achieve this. METHODS/DESIGN: This is a two-armed cluster-randomized clinical trial, with randomization among OPs. Forty OPs in two big companies providing medical care for multiple companies will be randomized to either the intervention group, i.e. psychiatric consultation embedded in a training programme, or the control group, i.e. only training aimed at recognition and providing Care As Usual. 60 patients will be included who have been absent from work for 6–52 weeks and who, after screening and a MINI interview, are diagnosed with depressive disorder, anxiety disorder or somatoform disorder based on DSM-IV criteria. Baseline measurements and follow up measurements (at 3 months and 6 months) will be assessed with questionnaires and an interview. The primary outcome measure is level of general functioning according to the SF-20. Secondary measures are severity of the mental disorder according to the PHQ and the SCL-90, quality of life (EQ-D5), measures of Return To Work and cost-effectiveness of the treatment assessed with the TiC-P. Process measures will be adherence to the treatment plan and assessment of the treatment provided by the Psychiatric Consultant (PC) in both groups. DISCUSSION: In the current study, a psychiatric consultation model that has already proved to be effective in the primary care setting, and aimed to enhance evidence-based care for patients with work absenteeism and common mental disorder will be evaluated for its efficacy and cost-effectiveness in the occupational health setting

Crop Updates 2009 - Farming Systems

This session covers nineteen papers from different authors: Decision support technology 1. The use of high resolution imagery in broad acre cropping, Derk Bakker and Grey Poulish, Department of Agriculture and Food 2. Spraywise decisions – online spray applicatiors planning tool, Steve Lacy, Nufarm Australia Ltd 3. Testing for redlegged earthmite resistance in Western Australia, Svetlana Micic, Peter Mangano, Tony Dore and Alan Lord, Department of Agriculture and Food 4. Screening cereal, canola and pasture cultivars for Root Lesion Nematode (Pratylenchus neglectus), Vivien Vanstone, Helen Hunter and Sean Kelly,Department of Agriculture and Food Farming Systems Research 5. Lessons from five years of cropping systems research, WK Anderson, Department of Agriculture and Food 6. Facey Group rotations for profit: Five years on and where to next? Gary Lang and David McCarthy, Facey Group, Wickepin, WA Mixed Farming 7. Saline groundwater use by Lucerne and its biomass production in relation to groundwater salinity, Ruhi Ferdowsian, Ian Roseand Andrew Van Burgel, Department of Agriculture and Food 8. Autumn cleaning yellow serradella pastures with broad spectrum herbicides – a novel weed control strategy that exploits delayed germination, Dr David Ferris, Department of Agriculture and Food 9. Decimating weed seed banks within non-crop phases for the benefit of subsequent crops, Dr David Ferris, Department of Agriculture and Food 10. Making seasonal variability easier to deal with in a mixed farming enterprise! Rob Grima,Department of Agriculture and Food 11. How widely have new annual legume pastures been adopted in the low to medium rainfall zones of Western Australia? Natalie Hogg, Department of Agriculture and Food, John Davis, Institute for Sustainability and Technology Policy, Murdoch University 12. Economic evaluation of dual purpose cereal in the Central wheatbelt of Western Australia, Jarrad Martin, Pippa Michael and Robert Belford, School of Agriculture and Environment, CurtinUniversity of Technology, Muresk Campus 13. A system for improving the fit of annual pasture legumes under Western Australian farming systems, Kawsar P Salam1,2, Roy Murray-Prior1, David Bowran2and Moin U. Salam2, 1Curtin University of Technology; 2Department of Agriculture and Food 14. Perception versus reality: why we should measure our pasture, Tim Scanlon, Department of Agriculture and Food, Len Wade, Charles Sturt University, Megan Ryan, University of Western Australia Modelling 15. Potential impact of climate changes on the profitability of cropping systems in the medium and high rainfall areas of the northern wheatbelt, Megan Abrahams, Chad Reynolds, Caroline Peek, Dennis van Gool, Kari-Lee Falconer and Daniel Gardiner, Department of Agriculture and Food 16. Prediction of wheat grain yield using Yield Prophet®, Geoff Anderson and Siva Sivapalan, Department of Agriculture and Food 17. Using Yield Prophet® to determine the likely impacts of climate change on wheat production, Tim McClelland1, James Hunt1, Zvi Hochman2, Bill Long3, Dean Holzworth4, Anthony Whitbread5, Stephen van Rees1and Peter DeVoil6 1 Birchip Cropping Group, Birchip, Vic, 2Agricultural Production Systems Research Unit (APSRU), CSIRO Sustainable Ecosystems, Climate Adaptation Flagship, Qld, 3 AgConsulting, SA 4 Agricultural Production Systems Research Unit (APSRU), CSIRO Sustainable Ecosystems, Toowoomba Qld, 5 CSIRO Sustainable Ecosystems, SA, 6 Agricultural Production Systems Research Unit (APSRU), Department of Agriculture and Fisheries, Queensland 18. Simple methods to predict yield potential: Improvements to the French and Schultz formula to account for soil type and within-season rainfall, Yvette Oliver, Michael Robertson and Peter Stone, CSIRO Sustainable Ecosystems 19. Ability of various yield forecasting models to estimate soil water at the start of the growing season, Siva Sivapalan, Kari-Lee Falconer and Geoff Anderson, Department of Agriculture and Foo

Reproducible radiomics through automated machine learning validated on twelve clinical applications

Radiomics uses quantitative medical imaging features to predict clinical outcomes. Currently, in a new clinical application, findingthe optimal radiomics method out of the wide range of available options has to be done manually through a heuristic trial-anderror process. In this study we propose a framework for automatically optimizing the construction of radiomics workflows perapplication. To this end, we formulate radiomics as a modular workflow and include a large collection of common algorithms foreach component. To optimize the workflow per application, we employ automated machine learning using a random search andensembling. We evaluate our method in twelve different clinical applications, resulting in the following area under the curves: 1)liposarcoma (0.83); 2) desmoid-type fibromatosis (0.82); 3) primary liver tumors (0.80); 4) gastrointestinal stromal tumors (0.77);5) colorectal liver metastases (0.61); 6) melanoma metastases (0.45); 7) hepatocellular carcinoma (0.75); 8) mesenteric fibrosis(0.80); 9) prostate cancer (0.72); 10) glioma (0.71); 11) Alzheimer’s disease (0.87); and 12) head and neck cancer (0.84). Weshow that our framework has a competitive performance compared human experts, outperforms a radiomics baseline, and performssimilar or superior to Bayesian optimization and more advanced ensemble approaches. Concluding, our method fully automaticallyoptimizes the construction of radiomics workflows, thereby streamlining the search for radiomics biomarkers in new applications.To facilitate reproducibility and future research, we publicly release six datasets, the software implementation of our framework,and the code to reproduce this study