A global study to identify a potential basis for policy options when integrating animal welfare into the UN Sustainable Development Goals

A previously developed methodology to rate the strength of the impact of improving animal welfare on achieving each of the 17 SDGs and the impact of achieving each SDG on animal welfare was used at the third Animal Welfare Global Forum of the World Organisation for Animal Health. Data from 95 participants from key stakeholder categories and organisations involved in animal welfare were analysed. The resulting ‘map’ of the relative strengths of these associations confirmed the expected co-benefits of improving animal welfare and achieving the SDGs. Differences at regional level and according to the economic classification of the country were also identified. This paper focuses on using this ‘map’ as a potential guide for how organisations interested in improving animal welfare could identify potential new allies for strategic partnerships to facilitate the implementation of different policy options. For example, a strategy can be to collaborate with those organisations where the impact is of similar mutual benefit, e.g. between improving animal welfare and achieving SDG 3 (Good health and well-being). Organisations in these two areas are already aligning themselves in the ‘One Health’ movement. Another strategy can be to align with organisations for whom achievement of their goal has the greatest impact on animal welfare, even if the impact is not mutual e.g. by collaborating with organisations working to achieve SDG 16 (Peace justice and strong institutions) and SDG 4 (Quality education). Achieving these goals was considered to have a large impact on improving animal welfare, equivalent to that of achieving SDG 3. In summary, this study can help organisations working in the area of animal welfare identify previously untapped areas of potential support, so tailoring their efforts efficiently, while at the same time themselves supporting movement towards the Agenda 2030. Simply put, the co-benefits make collaboration worthwhile, potentially opening up opportunities that would be unavailable when organisations are working independently towards their own respective goals

Why Are Outcomes Different for Registry Patients Enrolled Prospectively and Retrospectively? Insights from the Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF).

Background: Retrospective and prospective observational studies are designed to reflect real-world evidence on clinical practice, but can yield conflicting results. The GARFIELD-AF Registry includes both methods of enrolment and allows analysis of differences in patient characteristics and outcomes that may result. Methods and Results: Patients with atrial fibrillation (AF) and ≥1 risk factor for stroke at diagnosis of AF were recruited either retrospectively (n = 5069) or prospectively (n = 5501) from 19 countries and then followed prospectively. The retrospectively enrolled cohort comprised patients with established AF (for a least 6, and up to 24 months before enrolment), who were identified retrospectively (and baseline and partial follow-up data were collected from the emedical records) and then followed prospectively between 0-18 months (such that the total time of follow-up was 24 months; data collection Dec-2009 and Oct-2010). In the prospectively enrolled cohort, patients with newly diagnosed AF (≤6 weeks after diagnosis) were recruited between Mar-2010 and Oct-2011 and were followed for 24 months after enrolment. Differences between the cohorts were observed in clinical characteristics, including type of AF, stroke prevention strategies, and event rates. More patients in the retrospectively identified cohort received vitamin K antagonists (62.1% vs. 53.2%) and fewer received non-vitamin K oral anticoagulants (1.8% vs . 4.2%). All-cause mortality rates per 100 person-years during the prospective follow-up (starting the first study visit up to 1 year) were significantly lower in the retrospective than prospectively identified cohort (3.04 [95% CI 2.51 to 3.67] vs . 4.05 [95% CI 3.53 to 4.63]; p = 0.016). Conclusions: Interpretations of data from registries that aim to evaluate the characteristics and outcomes of patients with AF must take account of differences in registry design and the impact of recall bias and survivorship bias that is incurred with retrospective enrolment. Clinical Trial Registration: - URL: http://www.clinicaltrials.gov . Unique identifier for GARFIELD-AF (NCT01090362)

Risk profiles and one-year outcomes of patients with newly diagnosed atrial fibrillation in India: Insights from the GARFIELD-AF Registry.

BACKGROUND: The Global Anticoagulant Registry in the FIELD-Atrial Fibrillation (GARFIELD-AF) is an ongoing prospective noninterventional registry, which is providing important information on the baseline characteristics, treatment patterns, and 1-year outcomes in patients with newly diagnosed non-valvular atrial fibrillation (NVAF). This report describes data from Indian patients recruited in this registry. METHODS AND RESULTS: A total of 52,014 patients with newly diagnosed AF were enrolled globally; of these, 1388 patients were recruited from 26 sites within India (2012-2016). In India, the mean age was 65.8 years at diagnosis of NVAF. Hypertension was the most prevalent risk factor for AF, present in 68.5% of patients from India and in 76.3% of patients globally (P < 0.001). Diabetes and coronary artery disease (CAD) were prevalent in 36.2% and 28.1% of patients as compared with global prevalence of 22.2% and 21.6%, respectively (P < 0.001 for both). Antiplatelet therapy was the most common antithrombotic treatment in India. With increasing stroke risk, however, patients were more likely to receive oral anticoagulant therapy [mainly vitamin K antagonist (VKA)], but average international normalized ratio (INR) was lower among Indian patients [median INR value 1.6 (interquartile range {IQR}: 1.3-2.3) versus 2.3 (IQR 1.8-2.8) (P < 0.001)]. Compared with other countries, patients from India had markedly higher rates of all-cause mortality [7.68 per 100 person-years (95% confidence interval 6.32-9.35) vs 4.34 (4.16-4.53), P < 0.0001], while rates of stroke/systemic embolism and major bleeding were lower after 1 year of follow-up. CONCLUSION: Compared to previously published registries from India, the GARFIELD-AF registry describes clinical profiles and outcomes in Indian patients with AF of a different etiology. The registry data show that compared to the rest of the world, Indian AF patients are younger in age and have more diabetes and CAD. Patients with a higher stroke risk are more likely to receive anticoagulation therapy with VKA but are underdosed compared with the global average in the GARFIELD-AF. CLINICAL TRIAL REGISTRATION-URL: http://www.clinicaltrials.gov. Unique identifier: NCT01090362

Developing an Intelligent Educational Agent with Disciple

Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent to perform domain-specific tasks in a way that resembles how the expert would teach an apprentice, by giving the agent examples and explanations, and by supervising and correcting its behavior. The Disciple approach is currently implemented in the Disciple Learning Agent Shell. We make the claim that Disciple can naturally be used by an educator to build certain types of educational agents. The educator will directly teach the Disciple agent how to perform certain educational tasks and then the agent can interact with the students to perform such tasks. This paper presents the Disciple approach and its application to building an educational agent that generates history tests for students. These tests provide intelligent feedback to the student in the form of hints, answer and explanations, and assist in the assessment of students’ understanding and use of higher-order thinking skills

Teaching an Agent to Test Students

This paper presents an innovative application of the Disciple Learning Agent Shell to the building of an educational agent that generates history tests for middle school students, to assist in the assessment of their understanding and use of higher-order thinking skills. Disciple is an apprenticeship, multistrategy learning agent that can be taught by an expert how to perform domain-specific tasks in a way that resembles the way an apprentice would be taught by the expert. Disciple has been taught by an educator to generate and answer basic test questions and to explain the answers. From its interaction with the educational expert, Disciple has learned general rules that allow it to generate a large number of new test questions for students, together with hints, answers, and explanations of the answers. As a result, it can guide the students during their practice of higher-order thinking skills as they would be directly guided by the educator. The Disciple agent is also a useful tool f..

Animal welfare and the United Nations’ sustainable development goals—broadening students’ perspectives

The mutually beneficial relationships between improving animal welfare (AW) and achieving the United Nations (UN) sustainable development goals (SDGs) were further explored and compared to previous work. This was done in the context of a doctoral training course where students selected at least six SDGs and reasoned around their impact on AW and vice versa. Then, students rated the strength of the SDG—AW links. Lastly, students engaged in an assessment exercise. Students reported an overall mutually beneficial relationship between AW and all SDGs, yet with significant differences in strength for SDGs 4, 11, 10, 12 and 13 to that previously found by experts. Students considered SDG 12: Responsible consumption and production the most promising way to integrate AW targets. This study further supports the positive role of AW in the success of the UN’s strategy. Still, the magnitude of the anticipated impacts is modified by stakeholder, context and experience

The Disciple Learning Agent Shell and a Disciple Test Generation Agent

The Disciple Learning Agent Shell is a tool for developing intelligent agents where an expert teaches the agent how to perform domain-specific tasks in a way that resembles the way the expert would teach an apprentice, by giving the agent examples and explanations as well as by supervising and correcting its behavior. Disciple can be used by educators to build educational agents that will assist them in various ways. Such an educational agent that was built with Disciple generates history tests for middle school students, assisting the teacher in the assessment of students ’ understanding and use of higher-order thinking skills. The agent can also provide intelligent feedback to the student in the form of hints, answer and explanations. The test generation agent and the process of building it are presented in (Tecuci and Keeling, 1998). We propose to demonstrate both the Disciple shell and the test generation agent. 1. Disciple Learning Agent Shell Disciple is an apprenticeship, multistrategy learning approach for developing intelligent agents where an expert teaches the agent how to perform domain-specific tasks in a way that resembles the way the expert would teach an apprentice, by giving the agent examples and explanations a